Role of Intraoperative Frozen Section Diagnosis in the Management of Patients with CNS Lesions: Evaluation of 60 Consecutive Intraoperative Frozen Sections from 1991 to 1993
Bibliographic record
Abstract
To determine the role of intraoperative frozen sections (FSs) in the management of patients with central nervous system (CNS) lesions, 60 consecutive intraoperative clinical diagnoses of CNS lesions were presented and compared with concomitantly obtained FS diagnoses. Clinical diagnoses were established byhistory, physical examination, imaging techniques, and gross appearance of the abnormal tissue in situ. Tissue samples were obtained intraoperatively and processed for FS diagnoses. The findings of the FS diagnoses were reported to the operating room and compared with the clinical diagnoses. The remainingbiopsy samples were used to prepare paraffin-embedded tissue sections from which the definitive diagnoses were made. Comparison of the clinical and FS diagnoses, using paraffin-embedded tissue as the true diagnosis, shows that FS diagnosis has a limited contribution to intraoperative patient management by the neurosurgeon. The rate of diagnostic failures between the two techniques was very similar; clinical diagnoses and FSs were misinterpreted in 12 and 11 of the 60 cases, respectively. Compared to a clinical diagnosis, the intraoperative FS technique provided no significant improvement in diagnosis and management; it altered the intraoperative management of the patients in 2 of 60 cases.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".