Evaluation of Pain Measurement Practices and Opinions of Peripheral Nerve Surgeons
Bibliographic record
Abstract
The purpose of this study was to evaluate the opinions and practices of peripheral nerve surgeons regarding assessment and treatment of pain in patients following nerve injury. Surgeons with expertise in upper extremity peripheral nerve injuries and members of an international peripheral nerve society were sent an introductory letter and electronic survey by email ( n=133). Seventy members responded to the survey (49%) and 59 surgeons completed the survey (44%). For patients referred for motor or sensory dysfunction, 31 surgeons (52%) indicated that they always formally assess pain. In patients referred for pain, 44 surgeons (75%) quantitatively assess pain using a verbal scale ( n=24) or verbal numeric scale ( n=36). The most frequent factors considered very important in the development of chronic neuropathic pain were psychosocial factors (64%), mechanism of injury (59%), workers' compensation or litigation (54%), and iatrogenic injury (48%). In patients more than 6 months following injury, surgeons frequently see: cold sensitivity (54%), decreased motor function (42%), paraesthesia or numbness (41%), fear of returning to work (22%), neuropathic pain (20%), and emotional or psychological distress (17%). Only 52% of surgeons who responded to the survey always evaluate pain in patients referred for motor or sensory dysfunction. Pain assessment most frequently includes verbal patient response, and assessment of psychosocial factors is rarely included. Predominately, patient-related factors were considered important in the development of chronic neuropathic pain.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".