Patterns and Sources of Error in Intraoperative Neuropathology Diagnosis
Bibliographic record
Abstract
The objective of this study was to gain a better understanding of sources of error and diagnostic trends with increasing experience in intraoperative consultations for an individual neuropathologist. The first 100 (P1) and subsequent 100 (P2) intraoperative consultations performed by the study pathologist were reviewed. Using the final diagnosis as the gold standard, intraoperative diagnoses were scored as correct, or having minor or major misinterpretations on clinicopathological grounds. Misinterpreted cases were then reexamined by two independent reviewers to identify sources of error in each case. Twenty misinterpretations, were identified among the 200 cases: 11 in P1 and 9 in P2. Of these 20, 12 were classified as being of potentially major clinicopathological significance by one or both reviewers. Sampling, technical and diagnostic errors were all influential alone or in combination. There was a modest improvement in diagnostic accuracy over time and a trend towards fewer diagnoses deferred to permanent sections. The analysis of errors in intraoperative neuropathology diagnosis is a simple and practical self audit for surgeons and pathologists and a valuable learning exercise for trainees. Such studies also provide a clearer understanding of patterns and sources of error for a more informed approach to optimizing intraoperative consultations.
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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 | 0.009 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".