Assessing the safety and efficiency of a simplified diagnostic approach to deep vein thrombosis
Bibliographic record
Abstract
Background: Deep vein thrombosis (DVT) testing in the emergency department is complex and difficult to consistently apply. To address this, we developed the ToDay algorithm. Objectives: The objective was to assess the safety and efficiency of the ToDay algorithm. A secondary exploratory analysis evaluated applying different D-dimer thresholds. Methods: This prospective diagnostic management study was conducted at two emergency departments in one city. Patients with suspected DVT were potentially eligible for enrollment. Enrolled patients were followed by medical record review for 90 days. All venous thromboembolism (VTE) testing during follow-up was independently adjudicated. The primary outcomes were safety, measured by the failure rate, and efficiency of the ToDay algorithm. The failure rate is the proportion of patients with DVT ruled out at the index presentation that were diagnosed with VTE in the following 90 days. Efficiency is the proportion of patients that do not require an ultrasound to rule out DVT. Six other diagnostic strategies consisting of standalone D-dimers and D-dimers in low pretest probability patients were retrospectively assessed to estimate safety and efficiency. Results: Thirty-three (7.2%) out of 458 enrolled patients were diagnosed with DVT at their index visit. There were two follow-up VTE events. The ToDay algorithm had a failure rate of 0.5% (95% confidence interval (CI); 0.1 – 1.7%). The ToDay algorithm had an efficiency of 43.7% (95% CI; 39.2 – 48.2%). Using the six other simple diagnostic strategies, the failure rates ranged from of 0.0% to 0.7%. Using these six strategies, the efficiencies ranged from 30.6% to 61.8%. Conclusion: The ToDay algorithm had a failure rate of 0.5% and an efficiency of 43.7%. All other strategies had low failure rates with varying efficiencies.
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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 | 0.043 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".