Patient Perspectives on Interdisciplinary Peripheral Nerve Trauma Care
Bibliographic record
Abstract
Introduction: Peripheral nerve injury (PNI) is a complex, debilitating condition that is increasingly being treated in interdisciplinary clinics. Patients see peripheral nerve surgeons, neurologists, physiatrists, and electrodiagnostic technicians in a single encounter. No studies have evaluated patient experience within this unique interdisciplinary care model. This study aims to assess patients’ perceptions of the effectiveness of delivery of care and health care information in an interdisciplinary PNI clinic. Methods: A cross-sectional mixed-methods study was conducted using a 23-question survey that was by a lived-experience partner (an individual who had a brachial plexus injury) in research who helped design the survey. Participants attended an interdisciplinary clinic for PNI 1-2 days prior to taking the survey. The survey included 5-point Likert scales for measuring patient understanding and qualitative questions that were categorized into themes, using conventional content analysis. Results: Of the 20 participants, 65% were male, 35% were female and the mean age was 42.6 ± 17.8. Median scores of 4 were obtained for the patient understanding of the testing purposes, test results, nerve recovery after PNI, and surgical decision-making (full understanding = 5). On improving the clinical experience, 58% indicated no improvements were necessary, while 17% indicated the clinic felt rushed or overwhelming. When asked about positive aspects of their clinical experience, 64% appreciated the team approach to care, 27% valued the informative nature of the clinic, and 27% appreciated the progress they felt when providers at the clinic performed nerve testing. Conclusions: The results demonstrate that patients with PNI have overall positive perceptions of the delivery of care and information in an interdisciplinary PNI clinic. These results provide new insight into how interdisciplinary care may be beneficial to PNI patients based on the perceived effectiveness of knowledge translation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".