International Normalized Ratio Targets for Left-Sided Mechanical Valve Replacement
Bibliographic record
Abstract
Background Guidelines recommend higher international normalized ratio (INR) targets for patients with mechanical valves believed to be at higher risk for thromboembolism. Higher INR targets are associated with increased bleeding risk. We performed a systematic review and meta-analysis assessing effects of lower and higher INR targets on thromboembolic and bleeding risk in patients with mechanical heart valves. Methods We searched Cochrane CENTRAL, MEDLINE and EMBASE for randomized controlled trials (RCTs) evaluating lower versus higher INR targets for adults with bileaflet mechanical valves. We performed title and abstract screening, full-text review, risk of bias evaluation and data collection independently and in duplicate. We pooled data using a random effects model and used the Grading of Recommendations Assessment, Development and Evaluation framework to evaluate overall quality of evidence. Results We identified six RCTs (n = 5,497). Lower INR targets were associated with significantly less bleeding—22% versus 40% (relative risk [RR]: 0.54, 95% confidence interval [CI]: 0.31, 0.93, p = 0.03, very low quality). There was no difference in thromboembolism—2% in both groups (RR: 1.28, 95% CI: 0.88, 1.85, p = 0.20, very low quality) or mortality—5.5% with lower INR targets versus 8.5% (RR: 1.00, 95% CI: 0.82, 1.21, p = 0.47, moderate quality). Conclusion In patients with mechanical valves, higher INR targets are not supported by current evidence, which is of very low quality. In fact, our systematic review suggests that lower INR targets offer significantly lower bleeding risks with no significant difference in thromboembolic risk.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".