Bibliographic record
Abstract
#### Summary points Pulmonary embolism is one manifestation of venous thromboembolism, the other being deep vein thrombosis. Pulmonary embolism occurs when a deep vein thrombosis breaks free, passes through the right side of the heart, and lodges in the pulmonary arteries. About 90% of pulmonary emboli come from the legs, with most involving the proximal (popliteal or more central) veins. Prevention of pulmonary embolism therefore requires both prevention of venous thromboembolism and effective treatment of deep vein thrombosis when it occurs. There is a wealth of high quality individual studies and meta-analyses to guide the diagnosis and treatment of pulmonary embolism, and we provide an overview and synthesis of that evidence in this review. #### Sources and selection criteria We searched Medline and the Cochrane Collaboration for up to date systematic reviews, meta-analyses, and high quality randomised controlled trials pertaining to the epidemiology, diagnosis, and treatment of pulmonary embolism. We also drew on the recently published guidelines on diagnosis and treatment of pulmonary embolism from the National Institute for Health and Clinical Excellence (NICE) and on treatment for venous thromboembolism from the American College of Chest Physicians. Symptomatic venous thromboembolism occurs in 1–2 per 1000 adults each year, with about a third …
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".