Obesity as a Predictor for Pulmonary Embolism and Performance of the Age-Adjusted D-Dimer Strategy in Obese Patients with Suspected Pulmonary Embolism
Bibliographic record
Abstract
INTRODUCTION: Obesity is a risk factor for venous thromboembolism, but studies evaluating its association with pulmonary embolism (PE) in patients with suspected PE are lacking. OBJECTIVES: ) are associated with confirmed PE in patients with suspected PE and to assess the efficiency and safety of the age-adjusted D-dimer strategy in obese patients. METHODS: We conducted a secondary analysis of a multinational, prospective study, in which patients with suspected PE were managed according to the age-adjusted D-dimer strategy and followed for 3 months. Outcomes were objectively confirmed PE at initial presentation, and efficiency and failure rate of the diagnostic strategy. Associations between BMI and obesity, and PE were examined using a log-binomial model that was adjusted for clinical probability and hypoxia. RESULTS: We included 1,593 patients (median age: 59 years; 56% women; 22% obese). BMI and obesity were not associated with confirmed PE. The use of the age-adjusted instead of the conventional D-dimer cut-off increased the proportion of obese patients in whom PE was considered ruled out without imaging from 28 to 38%. The 3-month failure rate in obese patients who were left untreated based on a negative age-adjusted D-dimer cut-off test was 0.0% (95% confidence interval: 0.0-2.9%). CONCLUSION: BMI on a continuous linear scale and obesity were not predictors of confirmed PE among patients presenting with a clinical suspicion of PE. The age-adjusted D-dimer strategy appeared safe in ruling out PE in obese patients with suspected PE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".