Improving the quality of processing gastric cancer specimens: The pathologist's perspective
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Research into surgeon and pathologist knowledge of guidelines for lymph node (LN) assessment in gastric cancer demonstrated a knowledge deficit. To understand factors affecting optimal assessment we surveyed pathologists to identify external barriers. METHODS: Pathologists were identified using two Ontario physician databases and surveyed online or by mail, with a 40% response rate. RESULTS: The majority (56%) of pathologists stated assessing an additional five LNs would not be a burden. Most (80%) pathologists disagreed with pay for performance for achieving quality standards. Qualitative analysis determined the majority of pathologists believed achieving quality standards was inherent to their profession and should not require incentives. Poor surgical specimen was identified as a barrier and underscores the importance of aiming quality improvement initiatives at the multidisciplinary team. CONCLUSION: In addition to education, tailoring an intervention to address all barriers, including laboratory constraints may be an effective means of improving gastric cancer care.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".