The prevention of hospital‐acquired venous thromboembolism in the United Kingdom
Bibliographic record
Abstract
Hospital-acquired venous thromboembolism (VTE) remains the number one safety issue in hospitals and is estimated to cause more preventable deaths than the more publicized hospital-acquired infection. There has been a failure of implementation of thromboprophylaxis (TP), mainly because of lack of awareness among health professionals, despite the large number of evidence-based studies available. The situation in the UK is gradually changing because of tireless campaigning by politicians, a charity and key opinion leaders. In response, the Department of Health has issued a national risk assessment tool, and National Institute of Clinical Excellence (NICE) guidelines for the prevention of VTE in all hospitalised patients, which will be available in August 2009. Although NICE guidelines are only applicable in England, it is to be hoped Northern Ireland, Wales and Scotland will also follow. Despite this, the consensus of expert opinion is that TP needs mandating to prevent pockets of non-adherence. Low molecular weight heparins are currently the gold standard pharmacological agent for TP; but are likely to be superseded within the next 5 years by new classes of oral anticoagulants, such as dabigatran and rivaroxiban, which are already licensed for TP after orthopaedic surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".