The Relationship Between Preoperative International Normalized Ratio and Postoperative Major Bleeding in Total Shoulder Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess the relationship between preoperative international normalized ratio (INR) levels and major postoperative bleeding events after total shoulder arthroplasty (TSA). METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried for TSA from 2011 to 2020. A final cohort of 2405 patients with INR within 2 days of surgery were included. Patients were stratified into four groups: INR ≤ 1.0, 1.0 < INR ≤ 1.25, 1.25< INR ≤ 1.5, and INR > 1.5. The primary outcome was bleeding requiring transfusion within 72 hours, and secondary outcome variables included complication, revision surgery, readmission, and hospital stay duration. Multivariable logistic and linear regression analyses adjusted for relevant comorbidities were done. RESULTS: Of the 2,405 patients, 48% had INR ≤ 1.0, 44% had INR > 1.0 to 1.25, 7% had INR > 1.25 to 1.5, and 1% had INR > 1.5. In the adjusted model, 1.0 < INR ≤ 1.25 (OR 1.7, 95% CI 1.176 to 2.459), 1.25 < INR ≤ 1.5 (OR 2.508, 95% CI 1.454 to 4.325), and INR > 1.5 (OR 3.200, 95% CI 1.233 to 8.302) were associated with higher risks of bleeding compared with INR ≤ 1.0. DISCUSSION: The risks of thromboembolism and bleeding lie along a continuum, with higher preoperative INR levels conferring higher postoperative bleeding risks after TSA. Clinicians should use a patient-centered, multidisciplinary approach to balance competing risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".