The Ottawa Score Performs Poorly to Identify Cancer Patients at High Risk of Recurrent Venous Thromboembolism: Insights from the TROPIQUE Study and Updated Meta-Analysis
Bibliographic record
Abstract
The Ottawa score (OS) for predicting the risk of recurrent venous thromboembolism (VTE) in cancer patients with VTE may help to guide anticoagulant treatment decisions that will optimize benefit-risk ratios. However, data on its reliability are conflicting. We applied the OS to all cancer patients with VTE enrolled in the prospective multicenter TROPIQUE study who received low-molecular-weight heparin over a 6-month period. Of 409 patients, 171 (41.8%) had a high-risk OS. The 6-month cumulative incidence of recurrent VTE was 7.8% (95%CI 4.2-14.8) in the high-risk OS group versus 4.8% (95%CI 2.6-8.9) in the low-risk OS group (SHR 1.47; 95%CI 0.24-8.55). The Area Under the Receiver Operating Characteristic curve (AUROC) of the OS in identifying patients who developed recurrent VTE was 0.53 (95%CI 0.38-0.65), and its accuracy was 57.9%. Among individual variables included in the OS, only prior VTE was significantly associated with the 6-month risk of recurrent VTE (SHR 4.39; 95% CI 1.13-17.04). When pooling data from all studies evaluating this score for predicting VTE recurrence in cancer patients (7 studies, 3413 patients), the OS estimated pooled AUROC was 0.59 (95%CI 0.56-0.62), and its accuracy was 55.7%. The present findings do not support the use of the OS to assess the risk of recurrent VTE in cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".