Determinants of Success in Single- and Multi-Digit Replant
Bibliographic record
Abstract
Purpose: Understanding the variables that influence success in digital replant surgery is essential to guide clinical decision-making and to counsel patients. The purpose of this study was to determine the replant success rate and identify predictors of success at our tertiary care centre. Methods: This was a single centre, retrospective cohort study of consecutive patients who underwent digital replantation from January 2000 to September 2018. Adult patients with flexor zone I to III amputations were included. Patient demographics, comorbidities, injury pattern, operative data, and post-operative care were reviewed. The primary outcome was survival of the replanted digit at discharge. Results: A total of 146 patients met inclusion criteria. Of these, 100 had single-digit replants and 46 underwent multi-digit replants for a total of 220 digits. The success rate was 71%. Predictors of success included sharp mechanism of injury (P < .01), incomplete amputation (P < .01), amputation proximal to zone I flexor level (P = .02), post-operative acetylsalicylic acid use (P < .01), absence of leech use (P = .05), and absence of operative re-exploration (P < .01). Daytime replants had similar outcome compared to nighttime replants despite having increased ischemia time (7.9 ± 3.9 hours vs 6.8 ± 2.6 hours, P = .02). However, daytime operative time (7.8 ± 3.7 hours) was significantly shorter than nighttime replant time (9.6 ± 5.9 hours, P = .01). Conclusion: Sharp amputation, intact venous drainage, proximal amputation, and acetylsalicylic acid use were associated with replant survival and are factors to consider when managing patients for digital replantation. Leech therapy and operative re-exploration were associated with poor outcome. Nighttime replants required significantly longer operative time than daytime replants despite similar survival outcome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".