Predicting Deep Venous Thrombosis in Pregnancy: Out in “LEFt” Field?
Bibliographic record
Abstract
BACKGROUND: Clinicians' assessment of pretest probability, based on subjective criteria or prediction rules, is central to the diagnosis of deep venous thrombosis (DVT). Pretest probability assessment for DVT diagnosis has never been evaluated in pregnant women. OBJECTIVE: To evaluate the accuracy of clinicians' subjective assessment of pretest probability for DVT diagnosis and identify prediction variables that could be used for pretest probability assessment in pregnant women with suspected DVT. DESIGN: A cross-sectional study conducted over 7 years (March 2000 to April 2007). SETTING: 5 university-affiliated, tertiary care centers in Canada. PATIENTS: 194 unselected pregnant women with suspected first DVT. INTERVENTION: Diagnosis of DVT was established with abnormal compression ultrasonography at presentation or on serial imaging. Pretest probability by subjective assessment was recorded by thrombosis experts for each patient before knowledge of results. MEASUREMENTS: The sensitivity, specificity, negative predictive value, and likelihood ratios of subjective pretest probability assessment and their corresponding 95% CIs were calculated on the basis of the diagnosis of DVT. Patients were DVT positive if they had diagnostic compression ultrasonography at initial or serial testing or symptomatic venous thromboembolism on follow-up. Patients were DVT negative if they had negative compression ultrasonography at presentation and no venous thromboembolism on follow-up. A prediction rule for assessing DVT was derived, and an internal validation study was done to explore its performance. RESULTS: The prevalence of DVT was 8.8%. Clinicians' subjective assessment of pretest probability categorized patients into 2 groups: low pretest probability (two thirds of patients) with a low prevalence of DVT (1.5% [95% CI, 0.4% to 5.4%]) and a negative predictive value of 98.5% (CI, 94.6% to 99.6%), and nonlow pretest probability with a higher prevalence of DVT (24.6% [CI, 15.5% to 36.7%]). Three variables (symptoms in the left leg [L], calf circumference difference > or = 2 cm [E], and first trimester presentation [Ft]) were highly predictive of DVT in pregnant patients. LIMITATIONS: Few outcomes occurred. Altogether, 17 events were diagnosed during the study. The prediction rule derived should be validated on an independent sample before applying it to clinical practice. CONCLUSION: Subjective assessment of pretest probability seems to exclude DVT when the pretest probability is low. Moreover, 3 objective variables ("LEFt") may improve the accuracy of the diagnosis of DVT in pregnancy. Prospective validation studies are needed. PRIMARY FUNDING SOURCE: Heart and Stroke Foundation of Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".