Validation of the ToDay, a simplified diagnostic algorithm for deep vein thrombosis
Bibliographic record
Abstract
BACKGROUND: The current deep vein thrombosis (DVT) diagnostic algorithms are rarely followed in clinical practice due to complexity and time constraints. Simplified alternatives are needed to enhance adherence while maintaining diagnostic accuracy. The ToDay algorithm was developed to address these concerns by combining physician implicit assessment of DVT likelihood with D-dimer testing. OBJECTIVES: The objective of the study is to validate the ToDay algorithm using previously collected data. METHODS: This analysis used data from the 4D study (NCT02038530), a multicenter study evaluating DVT diagnostic strategies. The ToDay algorithm considers DVT excluded without further testing if DVT is considered most likely and D-dimer <500 ng/mL or if DVT is not considered most likely and D-dimer less than age-adjusted threshold. The primary outcome was 90-day symptomatic venous thromboembolism (VTE). Secondary outcome was not requiring ultrasound imaging. RESULTS: Among 1497 patients, 163 (10.9%) were diagnosed with DVT. Of the 1334 patients who had DVT excluded by the ToDay algorithm, 10 patients were found to have VTE during follow-up, a failure rate of 0.75% (95% CI, 0.41-1.37). Of all patients, 38.6% (95% CI, 36.2-41.1) did not require ultrasound imaging. CONCLUSION: The ToDay algorithm was found to be a safe and efficient alternative for DVT testing, reducing reliance on ultrasound imaging. It simplifies the diagnostic process, making it more feasible for emergency settings. Prospective validation is required before clinical adoption.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".