Core outcomes in nerve surgery: development of a core outcome set for ulnar neuropathy at the elbow
Bibliographic record
Abstract
OBJECTIVE: Ulnar neuropathy at the elbow (UNE) is common, affecting 1%-6% of the population. Despite this, there remains a lack of consensus regarding optimal treatment. This is primarily due to the difficulty one encounters when trying to assess the literature. Outcomes are inconsistently reported, which makes comparing studies or developing meta-analyses difficult or even impossible. Thus, there is a need for a core outcome set (COS) for UNE (COS-UNE) to help address this problem. The objective of this study was to utilize a modified Delphi method to develop COS-UNE. METHODS: A 5-stage approach was utilized to develop COS-UNE: stage 1, consortium development; 2, literature review to identify potential outcome measures; 3, Delphi survey to develop consensus on outcomes for inclusion; 4, Delphi survey to develop definitions; and 5, consensus meeting to finalize the COS and definitions. The study followed the Core Outcome Set-STAndards for Development (COS-STAD) recommendations. RESULTS: The Core Outcomes in Nerve Surgery (COINS) Consortium comprised 21 participants, all neurological surgeons representing 11 countries. The final COS-UNE consisted of 22 data points/outcomes covering the domains of demographic characteristics, diagnostics, patient-reported outcomes, motor/sensory outcomes, and complications. Appropriate instruments, methods of testing, and definitions were set. The consensus minimum duration of follow-up was 6 months, with the consensus optimal timepoints for assessment identified as preoperatively and 3, 6, and 12 months postoperatively. CONCLUSIONS: The authors identified consensus data points/outcomes and also provided definitions and specific scales to be utilized to help ensure that clinicians are consistent in their reporting across studies on UNE. This COS should serve as a minimum set of data to be collected in all future neurosurgical studies on UNE. The authors hope that clinicians evaluating ulnar neuropathy will incorporate this COS into routine practice and that future studies will consider this COS in the design phase.
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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 | 0.207 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".