Barriers to Surgical Intervention and Factors Influencing Motor Outcomes in Patients with Severe Peripheral Nerve Injury: A Province Wide Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Despite the importance of timing of nerve surgery after peripheral nerve injury, optimal timing of intervention has not been clearly delineated. The goal of this study is to explore factors that may have a significant impact on clinical outcomes of severe peripheral nerve injury that requires reconstruction with nerve transfer or graft. MATERIALS AND METHODS: Adult patients who underwent peripheral nerve transfer or grafting in Alberta were reviewed. Clustered multivariable logistic regression analysis was used to examine the association of time to surgery, type of nerve repair, and patient characteristics on strength outcomes. Cox proportional hazard regression analysis model was used to examine factors correlated with increased time to surgery. RESULTS: Of the 163 patients identified, the median time to surgery was 212 days. For every week of delay, the adjusted odds of achieving Medical Research Council strength grade ≥ 3 decreases by 3%. An increase in preinjury comorbidities was associated with longer overall time to surgery (aHR 0.84, 95% CI 0.74-0.95). Referrals made by surgeons were associated with a shorter time to surgery compared to general practitioners (aHR 1.87, 95% CI 1.14-3.06). In patients treated with nerve transfer, the adjusted odds of achieving antigravity strength was 388% compared to nerve grafting; while the adjusted odds decreased by 65% if the injury sustained had a pre-ganglionic injury component. CONCLUSION: Mitigating delays in surgical intervention is crucial to optimizing outcomes. The nature of initial nerve injury and surgical reconstructive techniques are additional important factors that impact postoperative outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".