Venous Thromboembolism After Acute Stroke
Bibliographic record
Abstract
BACKGROUND: Treatment for venous thromboembolism (VTE) is highly effective in preventing morbidity and mortality, yet pulmonary embolism (PE) accounts for up to 25% of early deaths after stroke. This is because the current diagnostic paradigm is reactive rather than proactive: the clinician responds to VTE when it becomes symptomatic, in the expectation that initiation of treatment will prevent progression to more serious manifestations. This approach is flawed, because sudden death from PE is frequently unheralded and nonfatal symptomatic pulmonary emboli are often unrecognized or misdiagnosed. SUMMARY OF COMMENT: Morbidity and mortality from PE could be reduced either by more effective thromboprophylaxis or earlier diagnosis and treatment of established VTE. The fact that early use of short-term, low-dose, unfractionated heparin (UFH) is not associated with sustained, clinically meaningful benefit suggests that a fundamental change in the diagnostic approach to VTE is needed, one which requires a greater appreciation that clinically apparent events are merely the tip of the thromboembolism iceberg. CONCLUSIONS: Research into a strategy of screening for subclinical VTE in these patients is needed, with a view to identifying a subgroup at risk of progression to symptomatic and life-threatening events, in whom outcome might be improved by anticoagulation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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 | 0.003 |
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".