F.10 Management of peripheral nerve sheath tumours: the Toronto Western Hospital experience
Bibliographic record
Abstract
Background: We retrospectively review benign peripheral nerve sheath tumours (BPNST) managed surgically at the Toronto Western Hospital. The incidence of BPNST is classified by anatomic location and predisposition syndrome. Independent predictors of tumour recurrence and symptom resolution are identified. Methods: 175 patients with 201 tumours were eligible for analysis. Data was collected on patient age, gender, diagnosis of neurofibromatosis (NF), tumour histopathology, tumour location, tumour volume, and extent of resection. Postoperative motor, sensory and pain outcomes were dichotomized as stable/improved or worse than preoperative scores. Relationships between tumour recurrence, or symptom resolution, and predictor variables were assessed with univariate and multiple logistic regression models. Results: Among Schwannomas, subtotal resection, a diagnosis of Schwannomatosis, and larger tumour volume were associated with recurrence (p=0.012, p=0.048, p=0.049, respectively); for neurofibromas, subtotal resection and a diagnosis of NF1 were associated with recurrence (p=0.036, p=0.022, respectively). Multivariate analyses revealed subtotal resection as an independent predictor of recurrence for BPNSTs (p=0.007, OR=13.16, 95%-CI 2.34-52.63). Gross-total resection (p=0.023, OR=4.01, 95%-CI 1.21-13.22) and presence of a preoperative motor deficit (p=0.038, OR=8.06, 95%-CI 4.65-90.91) were independent predictors of stable/improved postoperative motor function for BPNSTs. Conclusions: Gross-total resection is associated with both reduced recurrence and improved postoperative motor function, and should be attempted for all eligible BPNSTs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".