Assessment of the Intraoperative Consultation Service Rendered by General Pathologists in a Scenario Where a Well-Defined Decision Algorithm Is Followed
Bibliographic record
Abstract
OBJECTIVES: Intraoperative consultation (IOC) remains an area of general practice even within subspecialized pathology departments. This study assesses the IOCs rendered in a general pathology setting where surgeons integrate these results in a well-defined algorithm, developed with the input of specialized pathologists. METHODS: The surgical decisions to perform lymphadenectomy in patients with endometrial adenocarcinoma operated on at our institution between January 2003 and June 2015 as a result of the IOC assessment of tumor size, histologic grade, and depth of invasion in the hysterectomy specimen were analyzed. RESULTS: Frozen section (FS) was examined in 801 cases. In comparison to permanent section analysis, FS International Federation of Gynecology and Obstetrics (FIGO) grade had an overall accuracy of 0.95 (95% confidence interval [CI], 0.93-0.98). The FS depth of invasion had an overall accuracy of 0.92 (95% CI, 0.89-0.94). FIGO grade was not documented in 47.8%, the depth of myometrial invasion in 45.2%, and tumor size in 41.8% of the pathology reports. CONCLUSIONS: The high omission rate of the needed parameters by the general pathologists would question their overall understanding of the paradigm shift intended by this algorithm. Possible explanations of this phenomenon and potential solutions are discussed.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".