Sources of Error in Neuropathology Intraoperative Diagnosis
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to optimize intraoperative neuropathology consultations by studying trends and sources of diagnostic error. We hypothesized that errors in intraoperative diagnoses would have sampling, technical, and interpretive sources. The study also audited diagnostic strengths, weaknesses and trends associated with increasing experience. We hypothesized that errors would decline and that the accuracy of "qualified" diagnoses would improve with experience. METHODS: The pathologist's first 100 cases (P1), second 100 (P2), and most recent 100 (P3, after ten years in practice) formed the data set. Intraoperative diagnoses were scored as correct, minor error or major error using the final diagnosis as the gold-standard. Incorrect diagnoses were re-examined by two reviewers to identify sources of error. RESULTS: Among the 300 cases there were 22 errors with 11 in P1, 9 in P2 and 2 in P3. Sampling contributed to 17 errors (77%), technical factors to 7 (32%) and interpretive factors to 16 (73%). Improvement in diagnostic accuracy between P1 and P2 (p = 0.8143), or P2 and P3 (p = 0.0582) did not reach significance. However, significant improvement was found between P1 and P3 (p = 0.0184). CONCLUSION: The present study was a practical and informative audit for the pathologist and trainees. It reaffirmed the accuracy of intraoperative neuropathology diagnoses and informed our understanding of sources of error. Most errors were due to a combination of sampling, technical and interpretive factors. A significant improvement in diagnostic proficiency was observed with increasing experience.
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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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".