Identification of Medical Patients Who Should Receive Venous Thromboembolism Prophylaxis.
Bibliographic record
Abstract
Abstract Introduction Despite various medical illnesses/conditions and patient-related factors known to increase venous thromboembolism (VTE) risk in medical patients, there is no worldwide consensus regarding which of these patients should receive VTE prophylaxis. As a result, many medical patients remain at risk from this potentially fatal disease. Our objective was to develop a simple risk assessment model (RAM) that could be used at the bedside to identify medical patients who should receive prophylaxis. Methods Acute medical illnesses/conditions and risk factors were included in the RAM if there was strong evidence from prospective clinical studies to show that they significantly increase VTE risk in medical patients, or VTE prophylaxis was beneficial in these cases. If strong evidence was not available, the illness/condition or factor was only included if there was consensus from the authors that VTE prophylaxis is beneficial for these patients. Results Table 1 shows acute medical illnesses/conditions and factors associated with significant VTE risk that are included in the RAM. If a medical patient is >40 years old with an acute medical illness and reduced mobility and has one of the illnesses/conditions or factors shown in Table 1, the RAM recommends prophylaxis with low-molecular-weight heparin (LMWH: enoxaparin 40 mg o.d. or dalteparin 5000 IU o.d.) or unfractionated heparin (5000 IU q8h). LMWH is preferred due to a better safety profile. If pharmacologic prophylaxis is contraindicated, mechanical prophylaxis is recommended. Conclusion Acute medical illnesses/conditions and patient-related factors that increase the risk for VTE in medical patients have been identified and used to develop a novel RAM. The RAM is evidence-based wherever possible, and can be easily revised as new evidence becomes available. The RAM is simple in design, and can assist physicians to assess whether VTE prophylaxis is warranted in an individual medical patient. Table 1. Factors that increase the risk of VTE in medical patients Acute medical illnesses/conditions Risk factors *Note: The risk of hemorrhagic transformation should be assessed before giving VTE prophylaxis. Evidence-based: Acute MI, acute heart failure (NYHA III/IV), active cancer requiring therapy, severe infection/sepsis, respiratory disease (respiratory failure with/without mechanical ventilation, exacerbation of chronic respiratory disease), rheumatic disease (including acute arthritis of lower extremities, and vertebral compression), ischemic stroke*, paraplegia Consensus view only: Inflammatory disorder with immobility, inflammatory bowel disease Evidence-based: History of VTE, history of malignancy, concurrent acute infectious disease, age >75 years Consensus-based from strong evidence in other settings: Prolonged immobility, age >60 years, varicose veins, obesity, hormone therapy, pregnancy/postpartum, nephrotic syndrome, dehydration, thrombophilia, thrombocytosis
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".