Identification of Disappearing Brain Lesions With Intraoperative Magnetic Resonance Imaging Prevents Surgery
Bibliographic record
Abstract
BACKGROUND: Typically, neurosurgery is performed several weeks after diagnostic imaging. In the majority of cases, histopathology confirms the diagnosis of neoplasia. In a small number of cases, a different diagnosis is established or histopathology is nondiagnostic. The frequency with which these outcomes occur has not been established. OBJECTIVE: To determine the frequency and outcome of disappearing brain lesions within a group of patients undergoing surgery for suspected brain tumor. METHODS: Over the past decade, 982 patients were managed in the intraoperative magnetic resonance imaging unit at the University of Calgary, Calgary, Alberta, Canada. These patients have been prospectively evaluated. RESULTS: In 652 patients, a brain tumor was suspected. In 6 of the 652 patients, histopathology indicated a nontumor diagnosis. In 5 patients, intraoperative images, acquired after induction of anesthesia, showed complete or nearly complete resolution of the suspected tumor identified on diagnostic magnetic resonance imaging acquired 6 ± 4 (mean ± SD) weeks previously. Anesthesia was reversed, and the surgical procedure aborted. The lesions have not progressed with 6 ± 2 years of follow-up. CONCLUSION: Intraoperative magnetic resonance imaging prevented surgery on 5 patients with disappearing lesions.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".