A descriptive analysis of gastric cancer specimen processing techniques
Bibliographic record
Abstract
BACKGROUND: Adherence to guidelines for adequate gastric cancer specimen assessment is poor in North America. Inadequate staging and poor prognosis were noted in some series when these guidelines are not met. Recent advances have been made in standardizing cancer pathology reports in Canada; however, the uptake of these reporting systems is unknown for gastric cancer. A survey of pathologists in Ontario was performed to outline the processing techniques and practices for assessing gastric cancer specimens. METHODS: A survey was designed through a collaboration of surgical oncologists, general surgeons, pathologists, and research staff. Pathologists were identified using the College of Physicians and Surgeons of Ontario and MD Select databases. Participants were surveyed online or by mail-out. RESULTS: The response rate was 40.2% (147/366). Vascular invasion, perineural invasion, and signet ring cells were all reported as being examined for by the majority of pathologists. Fat clearing solution and keratin immunohistochemical techniques were not reported as being consistently utilized. Less than 70% of pathologists indicated using a form of synoptic report. CONCLUSION: Variations in practice and technique were observed. This may or may not reflect differences in quality of care or simply preferences for achieving equivalent results in the absence of standardized procedures. Education, evidence-based procedural guidelines and further research are required to provide infrastructure and support for pathologists and surgeons involved in the care of gastric cancer patients.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".