Management of peripheral nerve sheath tumors: 17 years of experience at Toronto Western Hospital
Bibliographic record
Abstract
OBJECTIVE A surgical series of 201 benign and malignant peripheral nerve sheath tumors (PNSTs) was assessed to characterize the anatomical and clinical presentation of tumors and identify predictors of neurological outcome, recurrence, and extent of resection. METHODS All surgically treated PNSTs from the Division of Neurosurgery at Toronto Western Hospital from 1993 to 2010 were reviewed retrospectively. Data were collected on patient demographics, clinical presentation, surgical technique, extent of resection, postoperative neurological outcomes, and recurrence. RESULTS One hundred seventy-five patients with 201 tumors had adequate follow-up for analysis. There were 182 benign and 19 malignant PNSTs. Of the benign lesions, 133 were schwannomas, 21 of which were associated with a diagnosis of schwannomatosis. There were 49 neurofibromas, and 26 were associated with neurofibromatosis Type 1 (NF1). Patients presenting with schwannomas were significantly older than those with neurofibromas. Schwannomas were more readily resected than neurofibromas, with the extent of resection of the former influenced by tumor location. Patients with benign PNSTs typically presented with a painful mass and less frequently with motor deficits. The likelihood of worsened postoperative motor function was decreased in patients with fully resected tumors or preoperative deficits. Recurrence of schwannomas and neurofibromas were seen more frequently in patients diagnosed with NF3 and NF1, respectively. Subtotal resection was associated with the increased recurrence of all benign lesions. CONCLUSIONS Outcomes following resection of benign PNSTs depend on tumor histopathology, tumor location, and genetic predisposition syndrome. Gross-total resection should be attempted for benign lesions where possible. The management of malignant PNSTs remains challenging, requiring a multimodal approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".