Factors Affecting the Decision to Initiate Anticoagulation After Spine Surgery: Findings From the AOSpine Anticoagulation Global Initiative
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional, international survey. OBJECTIVES: To identify factors influencing pharmacologic anticoagulation initiation after spine surgery based on the AOSpine Anticoagulation Global Survey. METHODS: This survey was distributed to the international membership of AOSpine (n = 3805). A Likert-type scale described grade practice-specific factors on a scale from low (1) to high (5) importance, and patient-specific factors a scale from low (0) to high (3) importance. Analysis was performed to determine which factors were significant in the decision making surrounding the initiation of pharmacologic anticoagulation. RESULTS: A total of 316 spine surgeons from 64 countries completed the survey. In terms of practice-specific factors considered to initiate treatment, expert opinion was graded the highest (mean grade ± SD = 3.2 ± 1.3), followed by fellowship training (3.2 ± 1.3). Conversely, previous studies (2.7 ± 1.2) and unspecified guidelines were considered least important (2.6 ± 1.6). Patient body mass index (2.0 ± 1.0) and postoperative mobilization (2.3 ± 1.0) were deemed most important and graded highly overall. Those who rated estimated blood loss with greater importance in anticoagulation initiation decision making were more likely to administer thromboprophylaxis at later times (hazard ratio [HR] = 0.68-0.71), while those who rated drain output with greater importance were likely to administer thromboprophylaxis at earlier times (HR = 1.32-1.43). CONCLUSION: Among our global cohort of spine surgeons, certain patient factors (ie, patient mobilization and body mass index) and practice-specific factors (ie, expert opinion and fellowship training) were considered to be most important when considering anticoagulation start times.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".