Enoxaparin for symptomatic COVID-19 managed in the ambulatory setting: An individual patient level analysis of the OVID and ETHIC trials
Bibliographic record
Abstract
Abstract Background Antithrombotic treatment may improve the disease course in non-critically ill, symptomatic COVID-19 outpatients. Methods We performed an individual patient-level analysis of the OVID and ETHIC randomized controlled trials, which compared enoxaparin thromboprophylaxis for either 14 (OVID) or 21 days (ETHIC) vs. no thromboprophylaxis for outpatients with symptomatic COVID-19 and at least one additional risk factor. The primary efficacy outcome included all-cause hospitalization and all-cause death within 30 days from randomization. Both studies were prematurely stopped for futility. Secondary efficacy outcomes were major symptomatic venous thromboembolic events, arterial cardiovascular events, or their composite occurring within 30 days from randomization. The same outcomes were assessed over a 90-day follow-up. The primary safety outcome was major bleeding (ISTH criteria). Results A total of 691 patients were randomized: 339 to receive enoxaparin and 352 to the control group. Over 30-day follow-up, the primary efficacy outcome occurred in 6.0 % of patients in the enoxaparin group vs. 5.8 % of controls for a risk ratio (RR) of 1.05 (95%CI 0.57–1.92). The incidence of major symptomatic venous thromboembolic events and arterial cardiovascular events was 0.9 % vs. 1.8 %, respectively (RR 0.52; 95%CI 0.13–2.06). Most cardiovascular thromboembolic events were represented by symptomatic venous thromboembolic events, occurring in 0.6 % vs. 1.5 % of patients, respectively. A similar distribution of outcomes between the treatment groups was observed over 90 days. No major bleeding occurred in the enoxaparin group vs. one (0.3 %) in the control group. Conclusions We found no evidence for the clinical benefit of early administration of enoxaparin thromboprophylaxis in outpatients with symptomatic COVID-19. These results should be interpreted taking into consideration the relatively low occurrence of events.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".