Systematic review and meta-analysis of test accuracy for the diagnosis of suspected pulmonary embolism
Bibliographic record
Abstract
Pulmonary embolism (PE) is a common, potentially life-threatening yet treatable condition. Prompt diagnosis and expeditious therapeutic intervention is of paramount importance for optimal patient management. Our objective was to systematically review the accuracy of D-dimer assay, compression ultrasonography (CUS), computed tomography pulmonary angiography (CTPA), and ventilation-perfusion (V/Q) scanning for the diagnosis of suspected first and recurrent PE. We searched Cochrane Central, MEDLINE, and EMBASE for eligible studies, reference lists of relevant reviews, registered trials, and relevant conference proceedings. 2 investigators screened and abstracted data. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. We pooled estimates of sensitivity and specificity. The review included 61 studies. The pooled estimates for D-dimer sensitivity and specificity were 0.97 (95% confidence interval [CI], 0.96-0.98) and 0.41 (95% CI, 0.36-0.46) respectively, whereas CTPA sensitivity and specificity were 0.94 (95% CI, 0.89-0.97) and 0.98 (95% CI, 0.97-0.99), respectively, and CUS sensitivity and specificity were 0.49 (95% CI, 0.31-0.66) and 0.96 (95% CI, 0.95-0.98), respectively. Three variations of pooled estimates for sensitivity and specificity of V/Q scan were carried out, based on interpretation of test results. D-dimer had the highest sensitivity when compared with imaging. CTPA and V/Q scans (high probability scan as a positive and low/non-diagnostic/normal scan as negative) both had the highest specificity. This systematic review was registered on PROSPERO as CRD42018084669.
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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 | 0.034 | 0.121 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.045 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".