009 Decision-analysis modelling of the effects of thromboprophylaxis for people with lower limb immobilisation for injury
Bibliographic record
Abstract
Background Pharmacological thromboprophylaxis reduces the risk of symptomatic venous thromboembolism (VTE) in people with lower limb immobilisation due to injury but can increase the risk of bleeding. We used decision-analytic modelling to compare the risks and benefits of thromboprophylaxis and determine the overall benefit of treatment. Method and results A decision-analytic model was developed to simulate the management of a cohort of people with lower limb immobilisation due to injury according to different thromboprophylaxis strategies, including thromboprophylaxis for all and thromboprophylaxis for none. Costs were estimated from the perspective of the UK National Health Service and Personal Social Services. A six-month decision tree was used to model rates of prophylaxis, VTE events (pulmonary embolism [PE], deep vein thrombosis [DVT]) and major bleeds). A Markov model with a lifetime horizon was used to extrapolate costs and QALY losses associated with chronic complications following VTE or bleeding events. The health states included within the Markov model captured the risk of post-thrombotic syndrome (PTS) following VTE and the risk of chronic thromboembolic pulmonary hypertension (CTEPH) following PE. QALYs were estimated by applying estimates of health utility to life expectancy after each of the events in the model. Conclusions The results suggest that the combined rate of serious acute adverse outcomes (intracranial haemorrhage [ICH], death from VTE or bleeding) would be around 1 in 4000 regardless of thromboprophylaxis use. As shown in table 1, the short-term benefits of thromboprophylaxis lie in reducing the rates of non-fatal PE, symptomatic DVT and asymptomatic DVT, with associated longer-term benefits of reduced risks of PTS and CTEPH. Overall, thromboprophylaxis is estimated to result in 0.015 additional QALYs per patient. Abstract 009 Figure 1 Predicted clinical outcomes per 100,000 patients with lower limb immobilisation due to injury Our findings suggest that the benefits of thromboprophylaxis lie in reducing long-term consequences of VTE rather than reducing the risk of acute serious adverse events.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".