Patient-reported Outcome Measures for Peripheral Nerve Injuries: A Systematic Review
Bibliographic record
Abstract
Background: The goal of managing patients with peripheral nerve injuries is to improve how a patient feels and functions. This goal is best assessed with patient-reported outcome measures (PROMs), which elicit patient concerns, treatment goals, and clinical progression. This study reviews existing PROMs for adult patients with peripheral nerve injuries to assess how comprehensively they measure outcomes important to patients. Methods: A systematic review of Ovid MEDLINE, Scopus, Web of Science, and Embase (from inception to August 13, 2022) was conducted to identify PROMs developed for adult patients with peripheral nerve injuries. Studies were included if (1) the study population involved traumatic or acquired peripheral nerve injuries; (2) they were randomized controlled trials, cohort studies, or single-arm observational studies; (3) participants were 18 years or older; and (4) PROMs were used to assess quality of life or patient satisfaction. Results: A total of 378 studies were included in this systematic review. We identified 141 unique PROMs used in the adult peripheral nerve injury literature: 20 are disease-specific (14%), 10 are function-specific (7%), 19 are mental health and well-being-specific (13%), 11 are quality of life-specific (8%), 32 are body region-specific (23%), 29 are symptom-specific (21%), 3 are satisfaction-specific (2%), 15 are generic (11%), and 2 are other (1%). Conclusions: There exists considerable heterogeneity of PROMs used in research on patients with peripheral nerve injuries. None of the PROMs comprehensively assess this patient population. The need for the development of a comprehensive PROM for this patient population is highlighted.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".