[Rationalization of risk factors for venous thromboembolism in medical inpatients. A prospective study].
Bibliographic record
Abstract
BACKGROUND: In terms of preventive management of venous thromboembolism in medical inpatients, very large differences may be observed. Rationalization of behaviour requires the evaluation of simple and logical parameters, which takes into account both patient safety and economic considerations. AIM: The aim of this study was to evaluate a preventive scheme including the rationalization of the indications and the use of low molecular weight heparin. EXPERIMENTAL DESIGN: Epidemiologic investigation. SETTING AND PATIENTS: Patients hospitalized in five medical departments in the Hospital Center of Nantes, France. INTERVENTION: The risk of venous thromboembolism was rated as high, intermediate and low. Patients with high or intermediate risk were eligible for prevention therapy (table I). MEASURES: The main criterion was the occurrence during hospital stay of deep or superficial venous thrombosis of the lower limbs, pulmonary embolism, or unexplained sudden death. The screening was based on clinical features double-checked by venous doppler ultrasonography of the lower limbs and/or ventilation-perfusion lung scanning. RESULTS: 24,497 patients were eligible (table II), 15% were considered at risk and treated with Nadroparin, 6% had the same risk profile but were not treated and 14. 7% had low risk and no prevention. No bleeding event was reported. The incidence of venous thromboembolism was 0.75%, 1.7% and 0.14% respectively (p <0.01) (table III). This efficacy does not appear to depend on body weight or the existence of multiple risk factors observed (table IV and V). CONCLUSIONS: This analysis of risk factors separates two populations with rates of incidence dramatically and significantly different. The prevention of venous thromboembolism by fixed dose of low molecular weight heparin remains justified since it reduces the risk of venous thromboembolism by a factor of 2.5.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".