Comparing Pathology Report Quality Indicators in 2 Distinct Whipple Resection Specimen Protocols
Bibliographic record
Abstract
OBJECTIVES: Pancreaticoduodenectomy specimens are complex, with varying gross examination techniques. In 2012, our institution began using axial sectioning. We sought to determine if this resulted in more complete pathology reporting. METHODS: Quality indicators were analyzed for pathology reports from 2 cohorts: 2001 to 2009 grossed traditionally and 2012 to 2017 using an axial technique (n = 81 and 51). Continuous and categorical data were compared using 2-tailed t test and Fisher exact test, respectively. RESULTS: The later cohort exhibited increased reporting of stage, lymphovascular invasion, margins/surfaces, mean number of lymph nodes, and mean number of slides (P < 0.01). No differences were seen in reporting of size, grade, or perineural invasion. In the later cohort, superior mesenteric vein/portal vein surface was positive in 17 cases (33%), showing strong correlation with superior mesenteric artery/uncinate margin involvement (13/17 cases; P = 0.0001). There was a higher rate of lymph node positivity (86% vs 65%, P < 0.01) in the later cohort. CONCLUSIONS: There is a trend toward higher-quality pathology reports in 2012 to 2017. A possible drawback of the axial approach is increased histopathology slides. Potential additional contributors include College of American Pathologists protocols, increasing subspecialty practice, and updates to the American Joint Committee on Cancer staging criteria.
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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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".