Core outcomes in nerve surgery: development of a core outcome set for sciatic injury and neuropathy evaluation
Bibliographic record
Abstract
OBJECTIVE: Core outcome sets (COSs) are needed to promote data consistency across studies as well as data synthesis and comparability. The goal of the current study was to utilize a modified Delphi process to develop a COS-sciatic injury and neuropathy evaluation (COS-SINE). METHODS: A five-stage approach was utilized to develop the COS-SINE: stage 1, consortium development; stage 2, literature review to identify potential outcome measures; stage 3, Delphi survey to develop consensus on outcomes for inclusion; stage 4, Delphi survey to develop definitions; and stage 5, consensus meeting to finalize the COS and definitions. The study followed the Core Outcome Set-STAndards for Development recommendations. RESULTS: The Core Outcomes in Nerve Surgery (COINS) Consortium comprised 23 participants, all neurological surgeons, representing 13 countries. Three participants were excluded on the basis of agreed upon participation rules. The final COS-SINE consisted of 36 data points/outcomes covering the domains of demographics, diagnostics, patient-reported outcomes, motor and sensory outcomes, and complications. Appropriate instruments, methods of testing, and definitions were set. The consensus minimum duration of follow-up was 24 months, with consensus optimal time points for assessment identified as preoperatively and 3, 6, 12, 24, and 36 months postoperatively. CONCLUSIONS: The COINS Consortium developed a consensus COS and provided definitions, methods of implementation, and time points for assessment. The COS-SINE should serve as a minimum set of data that should be collected in all future neurosurgical studies on sciatic nerve injury and neuropathy. Incorporation of this COS should help improve consistency in reporting and data synthesis and comparability and should minimize outcome-reporting bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".