Diagnostic accuracy of three ultrasonography strategies for deep vein thrombosis of the lower extremity: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Compression ultrasonography (CUS) is the first-line imaging test in the diagnostic management of suspected deep vein thrombosis (DVT) of the lower extremity. Three CUS strategies are used in clinical practice. However, their relative diagnostic accuracy is uncertain. OBJECTIVES: This systematic review and meta-analysis aimed to summarize and compare the diagnostic accuracy of single limited, serial limited, and whole-leg CUS for DVT. METHODS: MEDLINE, Embase, and CENTRAL were searched from January 1st, 1989 to July 23rd, 2019 for studies assessing at least one of the CUS strategies in adults with suspected DVT of the lower extremity, using clinical follow-up for venous thromboembolism or contrast venography as the reference standard. Study selection, data extraction, and risk of bias assessment were performed in duplicate by independent authors. A bivariate random-effects model was used to compute diagnostic accuracy summary estimates. RESULTS: Forty studies (n = 21,250) were included. The venous thromboembolic event rate after a negative CUS (failure rate) of single limited (1.4%; 95% CI, 0.83-2.5), serial limited (1.9%; 95% CI, 1.4-2.5), and whole-leg CUS (1.0%; 95% CI, 0.6-1.6) did not differ significantly. The proportion of positive results was lower with single limited CUS, as was DVT prevalence in this group. CONCLUSIONS: The failure rates of single limited, serial limited, and whole-leg CUS for DVT appeared to be quite comparable. The relative failure rate of single limited CUS remains uncertain, as the DVT prevalence was lower in these studies. Therefore, this CUS strategy may only be safe in a selected group of low-risk patients. Preference for one of the strategies may be based on pretest probability assessment, feasibility, expertise, and perceived clinical relevance of isolated distal DVT.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".