Intravenous heparin use in digital replantation and revascularization: The Quebec provincial replantation program experience
Bibliographic record
Abstract
BACKGROUND: No consensus exists among microsurgeons regarding the role of intravenous (IV) heparin in digital replantation/revascularization. The current experience of the Provincial Replantation Center in Quebec was reviewed over a 4-year period. METHODS: An initial retrospective review of all revascularized or reimplanted digits at our Replantation Center from April 2004 to April 2006 was conducted. Then, data of all patients treated at our center from January 08 to September 08 were prospectively collected. The two cohorts were compared with regards to demographics, injury characteristics, postoperative thromboprophylaxis medication as well as complication and success rates. Proportions were compared using χ(2) tests/Fisher's exact tests. Multivariate analysis was conducted with logistic regression. RESULTS: 175 digits were treated from April 2004 to April 2006, including 104 revascularizations and 71 amputations. IV heparin was used in 35.1% of the cases and was associated with a 3.59-fold (95% CI, 1.55-8.31) increase risk of developing a complication compared with cases where heparin was not used (P = 0.001). In 2008, 106 digits were treated. IV heparin was used in 14.6% of the cases and was not significantly associated with a higher complication rate compared with cases where heparin was not used (P = 0.612). Both cohorts' success rates were very similar (P = 0.557). The number of complications decreased from the first period (20.5%) to the second one (12.8%). CONCLUSION: Routine use of IV heparin following digital replantation and revascularization is not warranted. Surgical technique and type of injury remains the most important predictors for success in these complex procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".