Correspondence on “PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system” by Gui <i>et al</i>
Bibliographic record
Abstract
ABSTRACT Cancer rates vary widely across vertebrate groups. Identifying species with lower-than-expected cancer prevalence can help establish new models for unraveling the biological mechanisms underlying cancer resistance. Theoretical predictions suggest that cancer prevalence should be positively associated with body mass and longevity in animals. Yet, in mammals, the best studied vertebrates in terms of cancer, this prediction does not hold true: a phenomenon known as Peto’s paradox. Despite mounting work disentangling the biological basis of Peto’s paradox, it is still relatively unknown whether other major vertebrate groups behave similarly to mammals or might hold new keys to understanding cancer biology. Here, we present the largest dataset available so far on cancer prevalence across all major groups of tetrapod vertebrates: amphibians, birds, crocodilians, mammals, squamates (lizards and snakes), and turtles. We investigated cancer prevalence within and among these groups and its relationship with body mass and lifespan. This is the first study to analyze non-avian reptile groups separately. We found remarkably low cancer prevalence in birds, crocodilians, and turtles. Counter to previous studies, we found that body mass and lifespan are inversely related to cancer prevalence in mammals, although Peto’s paradox still holds true in this group. Conversely, we rejected Peto’s paradox in birds and squamates, as neoplasia prevalence was positively associated with body mass in these groups. The exceptionally low cancer prevalence in turtles and extensive variation in cancer prevalence amongst vertebrate families hold particular promise for identifying species with novel mechanisms of cancer resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".