P.114 Benign tumors of peripheral nerves in children at a tertiary-care pediatric hospital
Bibliographic record
Abstract
Background: Tumors affecting peripheral nerves in children are rare. Accurate diagnosis ensures that management is appropriate and timely. Methods: We review the clinical presentation and utility of investigations of children with intrinsic tumors affecting peripheral nerves at the Children’s Hospital of Eastern Ontario (CHEO). Results: From 2009-2019, 14 cases were identified. Mean age of symptom onset was 8.2 years (range 0.3 to 17.3 years). Presenting symptoms included painless muscle wasting (2/14), focal muscle weakness (7/14), contracture (1/14), pain (1/14) or a painless, palpable mass (3/14). MRI was useful at differentiating benign pediatric nerve tumors. Peripheral nerve lipomatosis demonstrated a classic “spaghetti string” appearance. Patients with perineurioma showed evidence of enhancing, nodular lesions while intraneural ganglionic cysts display cystic lesion within the nerve. Neurofibromas appear like a “bag of worms” while schwannomas are more eccentrically positioned around the nerve. Nerve conduction studies (NCS) or electromyography (EMG) were performed in 11/14 patients. Biopsies were performed in 9 patients and surgical management in 4 patients. Conclusions: The rare nature of peripheral nerve tumors in children can pose diagnostic challenges. NCS/EMG are important to assist with localization, and MRI important at distinguishing benign tumors. Key MRI, clinical and NCS features can guide management, potentially avoiding invasive procedures.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".