Role of Postoperative Anticoagulation in Predicting Digit Replantation and Revascularization Failure
Bibliographic record
Abstract
PURPOSE: The use of intravenous heparin after digit replantation or revascularization (DRR) varies greatly. The insufficient evidence presents a lack of clinical equipoise needed for a randomized trial; as such, a matched propensity score analysis was performed to evaluate the role of postoperative anticoagulation after DRR. The purpose of this study was to determine if the use of postoperative therapeutic anticoagulation reduced the risk of digit failure. METHODS: A retrospective cohort of patients who underwent DRR from 2005 to 2016 was identified. A propensity score was calculated based on age, smoking, injury mechanism, procedure type, vein graft, and number of digits injured. Patients were matched 1:2 by propensity score to create 2 groups with similar risks of receiving anticoagulation postoperatively. Generalized estimating equation logistic model was used to determine differences in digit failure between groups. RESULTS: Digit replantation or revascularization was performed on 282 patients (92% male; median age, 43 years). Postoperative anticoagulation was administered in 69 (24%) patients, with continuous IV heparin in 34 patients and intravenous heparin with dextran in 35 patients. Digit failure occurred in 88 patients overall, representing 38% of patients receiving anticoagulation and 29% of those not. Major complications were higher among the anticoagulated patients (13% vs 3.3%). After propensity score matching, use of anticoagulation was not associated with digit failure (odds ratio, 0.79; 95% confidence interval, 0.47-1.32). CONCLUSIONS: Among DRR patients with similar predisposing characteristics for postoperative therapeutic heparin or dextran, the use of therapeutic anticoagulation does not have a protective effect against digit failure. Studies are needed to define the role of postoperative IV anticoagulation in DRR and to justify the risk of its administration.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".