A pathologist's survey on the reporting of sessile serrated adenomas/polyps
Bibliographic record
Abstract
AIM: The purpose of this survey was to ascertain reporting habits of pathologists towards sessile serrated adenomas/polyps (SSA/P). METHODS: A questionnaire designed to highlight diagnostic criteria, approach and clinical implications of SSA/P was circulated electronically to 45 pathologists in the UK and North America. RESULTS: Forty-three of 45 pathologists agreed to participate. The vast majority (88%) had a special interest in gastrointestinal (GI) pathology, had great exposure to GI polyps in general with 40% diagnosing SSA/P at least once a week if not more, abnormal architecture was thought by all participants to be histologically diagnostic, and 11% would make the diagnosis if a single diagnostic histological feature was present in one crypt only, while a further 19% would diagnose SSA/P in one crypt if more than one diagnostic feature was present. The vast majority agreed that deeper sections were useful and 88% did not feel proliferation markers were useful. More than one-third did not know whether, or did not feel that, their clinicians were aware of the implications of SSA/P. CONCLUSIONS: 98% of pathologists surveyed are aware that SSA/P is a precursor lesion to colorectal cancer, the majority agree on diagnostic criteria, and a significant number feel that there needs to be greater communication and awareness among pathologists and gastroenterologists about SSA/P.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".