Rate and Predictors of Reclassification of Previously Diagnosed Hyperplastic Polyps to Sessile Serrated Adenomas in a City Wide Practice. Implications for Polyp Surveillance Recommendations
Bibliographic record
Abstract
Purpose: We evaluated the reclassification rate of recently diagnosed hyperplastic polyps (HP) to sessile serrated adenomas (SSAs) and the predictors of such reclassification. Methods: The provincial pathology database was searched for all colon polyps reported in the six pathology laboratories in the entire city in 2009. All retrieved pathological slides for previously reported right-sided HPs and a 20% random sample of left-sided HPs were reassessed by two pathologists with a special interest in gastrointestinal pathology. Polyp size, colonic location, age and sex of the study subjects were evaluated as potential predictors of reclassification. Results: 4,096 pathology reports by 25 different pathologists were reviewed. 20% of the polyps were reported as serrated colon polyps (SCPs). 17% of right-sided previously reported HPs and 20% of those >5 mm were reclassified as SSAs. Size > 5 mm [Odds ratio (OR): 4.2; 95% Confidence Interval (CI): 1.5-11.4] and location in the right colon [OR: 4.7; 95% CI: 1.4-15.4] were independent predictors of reclassification. Conclusion: A significant proportion of recently reported right-sided HPs may be SSAs. Surveillance recommendations for SCPs should consider the size and location of SCPs and not just the reported type.
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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 | 0.002 | 0.024 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".