Intraoperative consultation/final diagnosis correlation: relationship to tissue type and pathologic process.
Bibliographic record
Abstract
CONTEXT: The correlation of the diagnosis made at intraoperative consultation (IC) with the final diagnosis is one of the cornerstones of quality assurance in the anatomic pathology laboratory. OBJECTIVE: To review correlation of IC diagnoses with final diagnoses during a 1-year period in a regionalized, multisite hospital setting in a major Canadian city. DESIGN: One pathologist reviewed all surgical pathology cases at Calgary Laboratory Services from June 2004 through May 2005 that had an IC to extract the following data points: intraoperative diagnoses, final diagnoses, correlation between the two, anatomic site of the tissue on which the IC was requested, pathologic procedure requested of the IC, types of disagreements encountered, reasons for disagreement, and deferrals. RESULTS: Intraoperative consultations occurred for 2812 specimens, of which 87 were discordant and 135 were deferred. Percent agreement was 96.75% (95% confidence interval, 96.08-97.42) with a kappa statistic of 0.94 (95% confidence interval, 0.92-0.95). Lymph nodes for evaluation for metastases (427), thyroid/parathyroid (401), and central/peripheral nervous system (378) specimens were sent most frequently for IC, and the latter 2 tissue types accounted for the greatest number of disagreements. The most common assessments requested were the presence/typing of a neoplasm (1161) and assessment of margins (730), which also accounted for the largest number of disagreements. Disagreements were most frequently due to interpretive (53) and gross sampling (23) errors; false-negative disagreements were nearly 3 times as common as false positives. CONCLUSIONS: The IC was an excellent diagnostic test. Agreement and deferral rates varied by specimen site and by type of assessment requested.
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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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".