Gastrointestinal biopsies: more action and less ‘chatter’?
Bibliographic record
Abstract
‘Chatter’ is a histologic artifact which can obscure morphology, sometimes precluding histopathologic diagnosis. Chatter in gastrointestinal (GI) biopsies is a particular challenge in many laboratories. This departmental quality assurance initiative sought to determine (1) the prevalence of chatter in GI biopsies, (2) its relationship to histotechnologist, section level and anatomic site, and (3) the effectiveness of education and technical adjustments in reducing its rate. A gastrointestinal pathologist evaluated 660 randomly selected slides for chatter artifact. Biopsy site, level (slide 1,2 or 3) and sectioning histotechnologist were recorded. Histotechnologists completed a questionaire on block handling and sectioning technique. The study was repeated 6 months following feedback and implementation of technical measures to reduce chatter. Moderate-to-severe chatter was present in 9.2% of slides (61/660), with wide variation between the 11 histotechnologists (range 0-19%). All four histotechnologists with low chatter rates volunteered a constant sectioning speed in the questionaire, compared to 1/5 of those with high chatter rates (p < 0.05). Six months following implementation of measures to address chatter, its prevalence was reduced to 3.1% (9/291 slides), range 0-8% (p < 0.001). There was a higher prevalence of chatter in deeper levels (p < 0.02) and in colonic biopsies versus small intestinal biopsies (p < 0.05). In this study, chatter was operator dependent, less prevalent in histotechnologists reporting a constant cutting speed, and influenced by section level and site within the GI tract. In our experience simple technical measures and awareness were of value in reducing this troublesome artifact.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".