The clinical effectiveness and safety of intravenous unfractionated heparin following digital replantation and revascularization: A narrative systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Digital replants and revascularization (DRV) have been performed since the 1960s but there are no recognized standard peri-operative anticoagulation practices. A narrative systematic review of the clinical effectiveness and safety of therapeutic peri-operative unfractionated heparin following DRV was undertaken. METHODS: A review of the literature from 1985 to March 2022 was conducted using Medline, Embase, CINAHL and EBM reviews. Unfractionated heparin (UFH) use following DRV was compared to low-molecular weight heparin, other anticoagulants or no anticoagulation. Randomized trials, observational studies as well as guidelines were selected and independently screened. The Revised Cochrane risk-of-bias (RoB 2) tool and ROBINS-I were used to appraise risk of bias. RESULTS: While the search strategy identified 1490 references, only six studies met the inclusion criteria. Significant heterogeneity and the low methodological quality of the evidence precluded a meta-analysis. Among the four studies that documented the surgical success rate associated with the use of a therapeutic dose of UFH post DRV, only two reported improved clinical outcomes. Evidence of a higher complication rate related to UFH use was found in four studies. Low quality evidence suggests that a therapeutic dose of unfractionated heparin leads to a higher risk of complications when compared with heparin given as an intermittent bolus of unfractionated heparin or subcutaneous heparin, or prostaglandin E1 or no heparin. CONCLUSIONS: Current evidence suggests that IV UFH use following DRV has no significant impact on the success of the intervention. Heparin use may not be innocuous as some studies showed increased bleeding complications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it