Development of a Core Outcomes Set for Peripheral Nerve Injury
Bibliographic record
Abstract
Purpose: The contemporary literature evaluating outcomes after peripheral nerve injury (PNI) does not rigorously and adequately address the domains (motor, sensory, function, and pain) experienced by patients. Our goal was to develop a core outcomes set (COS) to evaluate outcomes after PNI. Methods: We adhered to recommended guidelines for COS development. Following a systematic review of the literature, we assembled a panel of experts and used a modified Delphi to assess the appropriateness of candidate measures to evaluate recovery after PNI. We convened 20 experts in PNI using two initial electronic surveys, one in-person meeting, and a final electronic survey. We arrived at consensus (≥70% of panelists) for required and recommended measures to evaluate outcomes after PNI. Results: Our panel arrived at consensus for motor, sensory, function, and pain outcomes in patients after upper and lower extremity nerve injury. We designated the measures to use and the timing and administration of these measures. Conclusions: We developed a COS that can be used by clinicians and researchers who evaluate patients with PNI. Our goal is to implement the COS in a unified manner, facilitating comparison in the literature as well as collaboration among centers. Type of study/level of evidence: Diagnostic V.
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 | 0.209 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".