Does Preoperative DVT Chemoprophylaxis in Spinal Surgery Affect the Incidence of Thromboembolic Complications and Spinal Epidural Hematomas?
Bibliographic record
Abstract
BACKGROUND: Deep venous thrombosis (DVT) and pulmonary embolus (PE) remain common surgical complications, often affecting patients without any prior warning. Postoperative spinal epidural hematomas (SEH) may have a devastating impact on a patient's recovery from a routine procedure. The effect of preoperative DVT prophylaxis administration on elective spinal patients has not previously been studied. STUDY DESIGN: Retrospective cohort analysis. OBJECTIVE: To correlate the incidence of preoperative DVT prophylaxis administration and the rate of postoperative DVT, PE, and SEH after elective spinal surgery. SUMMARY OF BACKGROUND DATA: Earlier studies have shown a postoperative DVT rate in elective spinal patients of between 0.3% and 31%, a PE rate of 0.2% to 0.9%, and a SEH rate of approximately 0.1%. METHODS: About 3870 patient notes, from 2004 to 2008 elective spinal procedures, were reviewed. DVT, PE, and SEH rates were compared between those patients receiving and not receiving preoperative DVT prophylaxis. RESULTS: The 36.9% of patients received preoperative DVT prophylaxis, and 19 patients suffered and DVT and/or PE. Nine of these had received preoperative prophylaxis, giving an odds ratio of 0.91. Sixteen patients suffered a SEH, and this gave an odds ratio of 1.33. The SEH's presented with a median postoperative time of 4 days. CONCLUSIONS: Preoperative DVT prophylaxis does not influence the rate of postoperative DVT or PE among elective spinal patients. It probably does not influence SEH rate, and it is noted that SEH may present quite late, in contrast to currently accepted time courses.
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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 | 0.011 |
| 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 | 0.001 | 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".