Determinants and prognostic impact of diagnostic delay in pulmonary embolism
Bibliographic record
Abstract
Introduction: Clinical presentation of pulmonary embolism (PE) is not specific and can lead to delayed diagnosis. Little is known about the determinants and the prognostic impact of this delay. Objectives: The aims of the study were to identify the determinants of the diagnostic delay and to evaluate its prognostic impact in patients with a first episode of PE. Methods: We conducted a retrospective analysis of a monocentric prospective cohort of patients with a first episode of PE. The diagnostic delay was defined as the median time from first symptom onset to diagnosis (> 3 days). Multivariable logistic regression analysis was performed to identify independant determinants of diagnostic delay. Prognostic impact was measured as the occurence of a 30-day adverse event including all-cause mortality, haemodynamic collapse or recurrent PE. Results: Among the 514 patients included, 240 (47%) had a diagnostic delay >3 days. Previous deep vein thrombosis (OR 0.56, 95%CI, 0.34-0.92), immobilization (OR 0.52, 95%CI, 0.29-0.92), surgery (OR 0.31, 95%CI, 0.16-0.60), chest pain (OR 0.55, 95%CI, 0.38-0.80), syncope (OR 0.43, 95%CI, 0.21-0.89), dyspnea (OR 2.39, 95%CI, 1.55-3.69) and hemoptysis (OR 3.67, 95%CI, 1.50-9.01) were independent risk factors for diagnostic delay. Twenty-two patients (4.3%, 95%CI, 2.8-6.5) had a 30-day complicated outcome: 15 patients (6.2%, 95%CI, 3.7-10.3) were in the diagnostic delay group and 7 (2.6%, 95%CI, 1.1-5.4) in the group without delay (p=0.039). Conclusions: Diagnostic delay is associated with the absence of major risk factors for PE or clinical features such as chest pain or syncope, and the presence of dyspnea or hemoptysis. This delay was associated with a worse 30-day prognosis.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".