Evaluation of muscle strength following peripheral nerve surgery: A scoping review
Bibliographic record
Abstract
Peripheral nerve injury (PNI) can result in devastating loss of function, often with poor long-term prognosis. Increased use of peripheral nerve surgical techniques (eg, nerve transfer, nerve grafting, and nerve repair) has resulted in improved muscle strength and other functional outcomes in patients with PNI. Muscle strength has largely been evaluated with the British Medical Research Council (MRC) scale. MRC is convenient to use in clinical settings, but more robust measures of muscle function are necessary to fully elucidate patient recovery. This scoping review aims to examine alternative instruments used to assess muscle function in studies of peripheral nerve surgery for PNI of the upper and lower limbs. A scoping review was conducted using Ovid MEDLINE, CINAHL, EMBASE, and PubMed databases in May and December of 2020, yielding a total of 20 studies pertaining to the review question. Studies pertaining to handheld dynamometry, grip and pinch dynamometry, Rotterdam Intrinsic Hand Myometers, isokinetic dynamometry, ultrasonography, and electromyography were reviewed. We provide a synopsis of each method and current clinical applications and discuss potential benefits, disadvantages, and areas of future research.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".