The Ottawa score for prediction of recurrent venous thromboembolism in cancer patients treated with tinzaparin: an individual patient data meta-analysis
Bibliographic record
Abstract
Background: Risk of venous thromboembolism (VTE) recurrence remains high in patients with cancer-associated thrombosis (CAT), despite therapeutic anticoagulation. Identifying patients at risk of treatment failure is still a challenge. Objectives: We aimed to assess the performance of the Ottawa score in predicting VTE recurrence in a large homogeneous population of patients with CAT treated with the same anticoagulant, tinzaparin, for at least 3 months. Methods: Individual patient data from 3 prospective cohort studies and 1 randomized controlled trial were pooled (PROSPERO: CRD42019119907). Clinical events of interest were adjudicated by independent central adjudication committees in all 4 studies. Results: Among the 1413 patients included, the Ottawa score could be calculated for 1088 of whom 646 (59.4%) were classified at high risk of recurrence (Ottawa score ≥ 1). The 6-month cumulative incidence of recurrent VTE was 5.0% (95% CI, 3.2-7.8) in the Ottawa low-risk group and 8.5% (95% CI, 6.6-10.8) in the high-risk group. The area under the receiver operating characteristic curve was 0.56 (95% CI, 0.51-0.62). The sensitivity of the dichotomized Ottawa score (score ≥ 1) was 72.8% (95% CI, 62.6%-83.0%), the specificity was 41.9% (95% CI, 37.8%-45.9%), the positive predictive value was 8.6% (95% CI, 6.4%-10.8%), and the negative predictive value was 95.3% (95% CI, 93.3%-97.4%). Introducing additional predictive factors failed to significantly improve the score's performance. Conclusions: Despite the large number of patients and anticoagulant treatment standardization, the Ottawa score failed to accurately predict recurrent VTE in patients with CAT treated with tinzaparin.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.075 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".