2051A meta-analysis of INR targets for mechanical heart valves: we need new evidence
Bibliographic record
Abstract
Background: Mechanical heart valves are more durable than tissue valves but require life-long anticoagulation. Guidelines recommend higher INR target ranges for valves in the mitral position compared with other positions and for patients deemed to be at higher risk for thromboembolism. Higher INR targets ranges are likely associated with increased bleeding risk. We performed a systematic review and meta-analysis of randomized control trials (RCTs) assessing the effect of high and low INR target ranges on thromboembolic and bleeding risk in adult patients with bi-leaflet mechanical heart valve replacement. Methods: We searched Cochrane CENTRAL, MEDLINE and EMBASE from 1975 to July 2016 as well as related reference lists and conference proceedings for RCTs evaluating low versus high INR target ranges for adults with bi-leaflet mechanical mitral and/or aortic valve(s). We performed title and abstract screening, full-text review, risk of bias evaluation, and data collection independently and in duplicate. We evaluated risk of bias for individual studies with the modified Cochrane (RCT) tool, overall quality of evidence with the GRADE framework, and pooled data using a random effects model in Revman 5.3. Event definitions were based on the definitions used in individual studies. We separated the data two different ways - 1) studies comparing lower versus higher INR target ranges and 2) studies comparing INR ranges with a median <3 versus ≥3.
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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 | 0.041 | 0.115 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".