Management of heterotopic ossification and venous thromboembolism following acquired brain injury
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of interventional strategies for the common complications of heterotopic ossification (HO) and venous thromboembolism (VTE) following acquired brain injury (ABI). METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2005 was conducted focusing on interventions for HO and VTE in the ABI population. Nineteen studies examining a variety of treatment approaches were evaluated. RESULTS: The majority of interventions are supported by limited evidence, defined as an absence of randomized controlled trials (RCTs). All of the treatment approaches for HO are supported with limited evidence. For VTE, there is moderate evidence, defined as at least one positive RCT, indicating that low-molecular-weight heparin is more effective than low-dose unfractionated heparin in preventing VTE, low-molecular-weight heparin is as effective and safe as unfractionated heparin for the prevention of pulmonary thromboembolism, low-molecular-weight heparin combined with compression stockings is more effective than compression stockings alone for the prevention of VTE and intermittent pneumatic compression devices are as effective as low-molecular-weight heparin for the prevention of VTE. CONCLUSIONS: There are a variety of intervention and prophylactic strategies that have been postulated to treat and reduce the incidence of these complications, with the goal of improving rehabilitation outcomes. It is therefore important to investigate the efficacy of these treatment strategies to provide guidance for clinical practice based on the best available evidence.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".