72THE IMPACT OF ADOPTING LOW MOLECULAR WEIGHT HEPARIN IN PLACE OF ASPIRIN AS ROUTINE THROMBOPROPHYLAXIS FOR PATIENTS WITH HIP FRACTURE
Bibliographic record
Abstract
Introduction: Deep vein thrombosis (DVT) is a significant cause of morbidity and mortality following hip fractures. National Institute of Health and Care Excellence guidelines recommended both mechanical and pharmacological measures (NICE CG96, 2010); calling for anticoagulant use rather than aspirin. We examine the impact of changing our unit’s pharmacological thromboprophylaxis policy in response to this recommendation. Methods: We examined data for patients presenting with hip fracture in a single UK trauma centre between 2007 and 2017; before and after our change in practice from use of aspirin to low molecular weight heparin (LMWH) in June-2010. Concern about the safety of compression stockings meant the rate of mechanical prophylaxis remained low across this period. We excluded 544/5,584 patients who normally reside outside our catchment area. Data for the remaining 5,039 was compared with medical physics records to all Doppler ultrasound scans they had undergone in this period. We examined the impact of the change of practice by calculating rates of lower-limb DVT in affected and unaffected limbs, both before and after hip fracture. Results: 913 patients (18.1%) received Doppler scans. 307 scans (33.6%) were prior to the fracture, but 400 were ‘relevant scans’ in the 180 days after a hip fracture. These identified 40 ipsilateral and 14 contralateral DVTs (p = <0.001). Fewer DVTs occurred after June-2010; 29/3,475 patients compared to 25/1,542 in previous years. The rate of DVT reduced significantly following the change in departmental policy; from 1.62% to 0.83% of patients with hip fracture (p = 0.012). Conclusions: This retrospective study suggests the rate of clinical DVT fell by half following this change in pharmacological prophylaxis. Our figure of <1% for the incidence of clinical DVT in a unit that routinely uses just LMWH following hip fracture will provide a context for discussions of alternative strategies, and for power calculations for future research.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".