You might have caught our recent announcement on the latest updates to Tessa, our AI-powered medical research analysis platform. Buried in the changelog is one decision that’s going to ruffle some feathers in academia: we completely removed the Author Score from our core trustworthiness rating (TScore) and redistributed its weight across our direct methodological metrics.

Here’s why we pulled the trigger, where author analytics live now instead, and why the traditional scientific hierarchy needs a reality check.

The Famous-Actor Fallacy

When we first built Tessa’s scoring logic across 200+ distinct data points, we included legacy benchmarks like Journal Impact Factor and author h-index. Why? Because that’s historically how the scientific community has evaluated credibility. If a paper came out of a high-profile lab with a heavily cited principal investigator, people assumed the work was solid.

Then we ran the numbers.

As Tessa processed thousands of papers across biomedical and healthcare literature, the data told a different story: an author’s h-index tracks publishing volume and career longevity, not the quality of any one paper.

Think of it like a Hollywood star. Two Oscars on the mantle didn’t stop your favorite actor from headlining last summer’s box-office disaster. A great track record is no guarantee the next project isn’t a dud.

In science, leaning on author prestige creates a dangerous halo effect: flawed methodology gets a free pass because a famous name sits on the title block. Good research has to stand on its own merits, so we stripped the author bias out. Now Tessa scores papers strictly on what actually matters: transparency, explainability, and significance — T.E.S.

Author History Isn’t Gone, It’s Just Not Grading the Paper

Removing the Author Score from the TScore doesn’t mean author history is irrelevant. Context still matters. It just shouldn’t be allowed to corrupt the objective evaluation of a single study.

So we built the Author Report: a dedicated dashboard, one click away from any author name in a Tessa Summary, with:

• A complete, interactive list of their published work
• Direct integration with their Google Scholar profile
• A standalone Author Score, built on i10-index principles (publications with 10+ citations)

The New Author Report

Full transparency: this standalone Author Score is still a work in progress. Like the h-index it’s modeled on, it doesn’t fully capture early-career researchers doing brilliant work, and it doesn’t properly weight the rare, seminal paper that racks up thousands of citations over time. We’re actively tuning the algorithm to better reflect real long-term contributions in future releases.

The difference is where this data lives. It’s context in a separate room, not a thumb on the scale of whether today’s paper is actually any good.

Science Over Status: What We’re Coming for Next

We expect pushback from academic traditionalists. The system has run on institutional prestige and author reputation for centuries, and disrupting that incentive structure is going to make some people uncomfortable.

That’s fine. We’re not here to protect academic egos, we’re here to fix how science gets validated. Good research is good research, no matter whose name is on it.

While we’ve got your attention: there’s another legacy prestige metric still sitting in our algorithm, and it’s next on the chopping block: the Journal Score.

The scientific community has long operated on the unproven assumption that a paper in a top-tier journal is inherently better than one in an open-access repository or a niche journal. As Tessa tears through thousands of new papers every week, we’re collecting the data to test that assumption directly.

Are top journals actually publishing better science, or are they just gatekeeping? How many high-rigor “sleeping beauties” are getting ignored simply because they didn’t land at a prestigious publication?

We have the data. Stay tuned.

No, sunscreen doesn’t cause cancer. You should use it.

You’ve probably seen these posts on X, LinkedIn, or Facebook. Someone claiming to be an “expert” or whistleblower insists that a common everyday product is secretly making you sick. They drop a few alarming numbers, point to a real scientific study, and rely on a simple trick: hoping you’ll take their word for it because the original paper looks too complicated to read.

Most of us don’t have the time or training to dig through 20 pages of dense scientific jargon. As a result, people accept these wild claims as facts. That’s how single posts end up with millions of views, thousands of shares, and dangerous health trends built on cherry-picked data.

AI often gets a bad rap for making things up or spreading fake news. But when it’s built with strict scientific rules, AI can actually be our best defense against fake science. Here’s how our AI tool, Tessa, exposed two viral posts claiming sunscreen causes skin cancer.

The Viral Claims: “Sunscreen is Poison!”

Recently, a wave of social media posts started targeting sunscreen, claiming it’s a dangerous scam that causes skin cancer and blocks essential Vitamin D. Two specific posts blew up:

1. The “Chemicals in the Bloodstream” Alarm: A post with over a million views cited a 2020 clinical study, claiming sunscreen active ingredients soak into your blood in under 24 hours at levels up to 500 times above FDA safety limits. The post argued that these chemicals act as hormone disruptors that cause skin cancer.
2. The “300% Cancer Surge” Claim: Another viral post cited a massive study of over 470,000 people, claiming it proved sunscreen users had a 292% higher risk of melanoma and a 140% higher risk of basal cell carcinoma.

To an average reader, these posts sound totally legit. They cite famous medical journals and huge studies. But what did the actual scientific papers say?

What Tessa Found: Solid Science, Terrible Interpretation

We ran both papers through Tessa, our AI system built on T.E.S. (Transparency, Explainability, and Significance).

Tessa doesn’t just skim a paper. It looks at hundreds of data points to create a trustworthiness score (TScore) from 0 to 100. Here’s how these papers scored:

• The Blood Absorption Study: Received a TScore of 82 (Green), showing it was a solid, well-run clinical trial.
• The UK Skin Cancer Study: Received a TScore of 72 (Green), showing it was a strong, well-designed observational study.

Both papers were actually good, reliable science. So why were those viral social media posts so dangerously wrong? Because the influencers cherry-picked isolated statistics and made up wild conclusions that the researchers never actually said.

Unpacking the Lies

Using Tessa’s deep-dive analysis, we stripped both studies down to their core facts to uncover the truth.

Case 1: “Absorbed in Blood” Doesn’t Mean “Toxic”

• The Claim: Active ingredients absorb into your blood and act as toxins that give you cancer.
• What the Science Found: The study tested 6 ingredients and found that blood levels did go above the FDA’s 0.5 ng/mL threshold after application.
• The Plain Truth: Just because a chemical enters the bloodstream doesn’t mean it’s harmful. The FDA set that 0.5 threshold as a trigger to do more safety testing, not as a toxic limit. In fact, the study authors and the FDA specifically noted that people should NOT stop using sunscreen, because protecting your skin from UV rays is crucial.

Effect of Sunscreen Application on Plasma Concentration of Sunscreen Active Ingredients: A Randomized Clinical Trial.

Case 2: The “Sunscreen Paradox”

• The Claim: Using sunscreen dramatically raises your chances of getting skin cancer.
• What the Science Found: Researchers studied how DNA repair genes work together with lifestyle habits like using sunlamps or getting sunburns as a kid.
• The Plain Truth: While the raw numbers showed that frequent sunscreen users had higher cancer rates, the researchers never said sunscreen causes cancer. Instead, they explained what scientists call the “Sunscreen Paradox”:
  • Who wears sunscreen? People with pale skin who burn easily, or people who already had skin cancer in the past, report using sunscreen far more often.
  • A False Sense of Security: Many people put on sunscreen and then stay out in the blazing sun for hours longer than they should, or they don’t put on enough.
  • What the authors recommended: The researchers urged people to reapply sunscreen frequently and seek shade, not dump their sunscreen in the trash.

Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk.

How Objective AI Gives Power Back to You

When we built Tessa at Siensmetrica, we wanted to give everyday people the power to spot trustworthy science without needing an advanced degree.

Tessa instantly cuts through the confusion by giving every paper a color-coded Score, so you can tell how solid the research is with just a glance.

But we don’t just hand you a number and expect you to take our word for it. If you’re curious (or skeptical) Tessa reveals the exact evidence behind that score. Nothing is hidden, and there are no black-box secrets. If Tessa gives a study a perfect sub-score for its experimental design, it shows you why: pointing directly to the study’s real-world data, clinical trial setup, and measurement methods.

Scientific researchers love these detailed breakdowns because they reveal the true quality of a study and put doubts to rest. We think regular people deserve that exact same clarity, transparency, and peace of mind. We want to give regular people the power to spot trustworthy science, without needing a medical degree.

The Bottom Line

Sensational headlines will always get clicks, but when bad information pretends to be “science,” it can make people sick. Objective AI gives us the power to cut through the noise, spot cherry-picked data, and get the facts in seconds.

As for the online panic? No, sunscreen doesn’t give you cancer. Please wear it, reapply it, and enjoy the sun safely. Your dermatologist will thank you.

See The Tessa Analysis for Yourself

Interested in how Tessa scored these papers and why? Here are the links to the Summary analysis for both (includes links to the published articles):

• Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk.
• Effect of Sunscreen Application on Plasma Concentration of Sunscreen Active Ingredients: A Randomized Clinical Trial.

Since the commercial release of ChatGPT back in 2022, medical AI has lived in an uncomfortable middle ground stuck somewhere between “whatever” and “have to have it”.

There’s been plenty of promising AI “stuff” — strong academic papers, impressive demos, limited, task-specific successes. But very little that clinicians or researchers could genuinely trust or want to use day-to-day.

That may finally be changing.

The growing interest in a medical large language model called Lingshu-7B is a meaningful signal. On Hugging Face, it’s been downloaded more than five times as often as the next most popular medical model, ClinicalBERT. Not because it introduced yet another a “breakthrough” architecture, but because it reflects a deeper shift in how medical AI is being built.

That shift is simple, but profound: Medical AI is moving from siloed pattern matching toward staged knowledge accumulation, much like human clinicians learn medicine.

The Shift From One-Off Training to Cumulative Understanding

Most early medical AI systems followed a familiar path:

1. Train on a scoped dataset.
2. Optimize for a specific task.
3. Publish results.
4. Move on.

Chest X-rays. Skin lesions. Retinal scans. One domain at a time.

This approach works for academic benchmarks, but it fails in real-world medicine, where clinicians integrate imaging, lab data, patient history, evolving research, oh, and uncertainty, all at once.

The Lingshu model takes a different approach.

Instead of treating each medical task as an isolated problem, it’s trained in multiple stages, with each stage building on what came before:

• Foundational alignment that teaches the model what medical data is without degrading general language understanding
• Deep multimodal alignment that connects images, text, and structured data across domains
• Instruction tuning that reflects how clinicians reason, explain, and document
• Rigorous evaluation across many clinical contexts using a unified framework

This mirrors how humans learn medicine. Foundations first. Then specialization. Then experience. Then judgment based on all the previous.

Why This Matters More Than Model Size or Leaderboards

The most important thing about Lingshu isn’t its parameter count or benchmark scores, it’s that developers are actually using it. And that tells us something critical about where healthcare AI is heading.

Clinicians and builders don’t want clever experiments. They want systems that reduce uncertainty, lower evaluation burden, and work reliably across real clinical situations.

The fact that AI developers are finally taking note and responding means that Medical AI is moving out of the experimental phase and into a status where evaluation, thoroughness, and trust matter more than cleverness and novelty.

The Same Shift Is Happening in Biomedical Research

At Siensmetrica, we see the same transition happening on the research and analysis side.

Our Tessa Neural Networking and AI platform is built on a similar principle: knowledge accumulated in stages and evaluated using scientific rigor. Instead of treating each paper or dataset in isolation, Tessa:

• Accumulates knowledge over time
• Applies transparent, repeatable scientific methodologies
• Evaluates new research, especially work that is early, novel, or not yet fully vetted

This matters because biomedical research dissemination and discussion now moves faster than traditional validation pipelines can keep up with. By the time a study completes peer review and replication, it may already be influencing clinical thinking, funding decisions, or patient care. Having a knowledgeable AI rather than a single or general purpose on-hand is a game changer.

Like Lingshu, Tessa isn’t trying to replace experts. it’s designed to support better judgment by bringing accumulated knowledge and clear evaluation to situations where humans are overwhelmed by volume and speed.

AI Is Reaching the Front Lines of Medicine

The bigger story here isn’t about a single model or platform. It’s about a transition.

AI in healthcare is moving beyond academic experiments, demos, and clever apps and into practical use by people who treat patients, interpret evidence, and make real decisions under time pressure.

Practical tools are rarely the flashiest. But they are the ones that learn in stages, show their work, make uncertainty visible, and earn trust through evaluation rather than claims. That is where medical AI is heading, and it’s exactly where it needs to go.

“Enteral Ventilation” was strange enough to get scientist’s attention. Turns out, they might be on to something

In testing Tessa’s ability to accurately evaluate and score medical research, people run a lot of papers seeing if they can trip up the platform’s analysis. We’ve seen pay-to-publish studies from sketchy device manufacturers, self-citing paper mill junk, retracted works, and studies 100% manufactured by AI.

So you can imagine what the testing guys thought when they were asked to feed Tessa a paper noted on Medicalxpress.com that looked at enteral ventilation, aka “butt breathing.”

The study was weird enough that it garnered an Ig Nobel Prize (the “Igs”) in 2024 from the Annals of Improbable Research. The award highlights discoveries at are surprising but also make people laugh (and think). However, it was just a preliminary one, designed to determine whether enteral ventilation was safe rather than effective. But, it did reference one from 2021, Mammalian enteral ventilation ameliorates respiratory failure which did test whether mammals are capable of breathing through their butts.

We gave that one to Tessa. The results lifted a few eyebrows.

The Paper Scored Well

First off, Tessa gave the paper an overall score of 77 out of 100 for trustworthiness, a very solid score—in the top 15% of papers. (Papers with a 90 or above are extremely rare, and Tessa has never given a 100.) A 77 indicates the science and methodologies are well-grounded and thorough and meet thresholds for transparency, explainability, and significance (T.E.S.). It’s other factors such as cohort size, rigor, the authors’ or publication’s reputation, etc. that took away from its overall, but still very respectable, score.

More interesting, Tessa’s analysis of the work scored it quite highly. An 8 of 10 in the “Theoretical-to-Empirical” and a 9 of 10 in the “Known-to-Novel” categories. Numbers that high are unusual, which means it’s a foundational work definitely worthy of more study. In the words of one of our engineers, “mammalian butt breathing is legit.”

If there’s a takeaway from this, it’s that people talking out their a*ses might just be a figure of speech, but enteral ventilation is a real thing.

If you’re interested, you can read Tessa’s full analysis of the paper below.

Tessa Analysis Results

Content Analysis

Research Question

Can the distal gut be repurposed as an accessory respiratory organ in mammals, delivering oxygen via the anus to alleviate systemic hypoxia and rescue respiratory failure?

Main Contribution

The study demonstrates, for the first time, that enteral ventilation via the anus (EVA) using either oxygen gas or oxygen-saturated perfluorocarbon can achieve clinically meaningful systemic oxygenation in mice and pigs with respiratory failure, improving survival and physiological parameters without major adverse effects, and thus proposes a novel, adjunctive route for respiratory support.

Theoretical to Empirical: 0.80

The study is almost entirely data-driven: it reports controlled experiments in mice, rats and pigs, including survival curves, arterial/venous blood-gas measurements, behavioural tracking and histopathology (pp.4–8). Safety assays such as endotoxin quantification, serum chemistry and GC-MS for drug levels are also presented (pp.7–9). These multiple in-vivo experiments make the paper highly empirical. However, it stops at pre-clinical animal work—there are no human subjects or randomized clinical trials—so it does not reach the very top end of empiricity.

Weak to Rigorous: 0.56

The paper presents a provocative proof-of-concept that mammalian distal gut can be repurposed for gas exchange. Across small-rodent and porcine models, both gas and liquid EVA increased systemic oxygenation and, in mice, markedly improved survival during lethal hypoxia. Strengths include multi-species validation, use of both functional (survival, behavior, SpO2) and biochemical (blood gases) endpoints, and an initial safety assessment. However, many experiments rely on very small sample sizes with limited randomization/blinding, short observation periods, and single time-point toxicity assays, which constrain statistical power and translational certainty. The requirement for mucosal abrasion in g-EVA limits clinical feasibility, while l-EVA efficacy, though encouraging, achieved modest PaO2 gains (~13 mm Hg) in pigs. Overall, the evidence supports feasibility but further studies with larger cohorts, longer follow-up, and disease-relevant models are needed to establish efficacy and safety.

Known to Novel: 0.90

Using the distal intestine as a functional respiratory organ in mammals had been largely speculative, with only scattered, historic or non-mammalian observations. This study is the first to show reproducible, lifesaving systemic oxygenation through enteral delivery of either oxygen gas or oxygen-saturated perfluorocarbon in two mammalian species, including a clinically relevant pig model, and to document safety data. It therefore opens an entirely new, previously unexplored route for respiratory support, representing a highly original research question and contribution.

Summary

Overall, the paper is scientifically sound and well executed. While the Authors’ collective h-index and Journal’s impact score pulled the overall score slightly lower, the Methods/Quality score, which forms the bulk of the overall score, is quite strong. Moreover, the paper’s Authentic score shows that no AI was used to help create it. The Media Score (coverage and discussion of paper online) is solid. If the topic of enteral ventilation continues to draw positive attention, this score will likely rise.

The full text of this paper is available here: https://www.cell.com/med/fulltext/S2666-6340(21)00153-7

The debate over reforming the National Institutes of Health is gaining urgency, we have some ideas

National Institute of Health Building 10

Writing on STATNews.com, the Good Science Project’s Stuart Buck outlines how administrative waste, irreproducible studies, and a risk-averse peer review system hold back progress. These challenges don’t just affect researchers, they also slow the pace of medical breakthroughs. At Siensmetrica, we believe reform is essential, but so is giving scientists better tools to navigate today’s overwhelming research landscape.

The NIH holds an enormous role in advancing medical science, but as Stuart Buck notes, much of that potential is weighed down by inefficiency and poor incentives. Three challenges stand out:

• Administrative waste: Researchers often spend close to half their time on compliance paperwork instead of science. That is wasted time and money.
• The reproducibility crisis: Studies are too often published without strong validation. Entire fields can be built on findings that don’t hold up.
• Peer review bias: Innovative work is often overlooked because the system favors “safe” projects with predictable outcomes.

These problems don’t just affect researchers, they also slow progress on medical advances that cost lives every day.

Attacking the Issue From the Ground

At Siensmetrica, we see this from the ground level. Scientists struggle to find reliable, relevant studies in a sea of questionable work. Our Tessa platform helps address this by scoring research across multiple dimensions: peer review quality, journal impact, author h-index, study novelty, reproducibility, as well as many more. This gives researchers a way to filter massive amounts of literature quickly and focus on the studies that are truly trustworthy.

In practice, this matters a great deal. When the volume of low-quality research grows, the best studies get buried. Researchers spend more time sifting through noise, which drains time and funding. Both are limited, and both are critical for advancing science.

Reforms like those Buck suggests—reducing bureaucracy, funding replication, and rethinking peer review—are a strong step toward fixing NIH’s structural problems. But tools outside government have a role too. Platforms like Tessa can help the research community cut through the clutter, reduce wasted effort, and accelerate discovery.

The future of science depends on both institutional reform and better tools for researchers. NIH reforms may clear some of the structural roadblocks. In the meantime, we can make progress by equipping scientists with better ways to navigate the research landscape.