The success of salvage procedures for failing digital replants: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The success of salvage procedures for failing digital replants (FR) is poorly documented. We sought to evaluate the success of salvage procedures for FR and factors contributing to successes and failures of replants. METHODS: Adult patients who presented to our center between January 1, 2000 and December 31, 2015, suffered ≥1 digital amputation(s), and underwent digital replantation were included. Preoperative, perioperative, and postoperative details were recorded. Digits were monitored postoperatively via nursing and physician assessments. The presumed reason for failure, details, and outcomes of salvage attempts were recorded for FR. Length of hospital stay and complications were also recorded. RESULTS: Fifty-two patients and 83 digits were included. Fifty-two digits (63%) were compromised (arterial ischemia in 15 digits; venous congestion in 37 digits) and 48 digits had salvage therapy. Twenty-one FR (44%) were salvaged via operative (1 of 2; 50%), nonoperative (19 of 43; 44%), and combined (1 of 3; 33%) therapies. FR patients were more likely than those with successful replants to receive a blood transfusion (52 vs. 23%; p = .009) with more transfused units (3.45 ± 3.30 vs. 0.86 ± 0.95; p = .001). Length of stay was prolonged for FR patients (9 [range: 2-22] vs. 7 [range: 3-19] days; p = .039). Ultimately, 59% (49 of 83) of replants were successful, where 25% (21 of 83) were successfully salvaged. CONCLUSION: Nonoperative and operative salvage therapies improve the rate of replant survival. We suggest close postoperative monitoring of all replants and active salvage interventions for compromised replants in the postoperative period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".