The Impact of Preoperative Anemia on Complications After Total Shoulder Arthroplasty
Bibliographic record
Abstract
BACKGROUND: This study investigated the relationship between varying levels of preoperative anemia and postoperative complications within 30 days of total shoulder arthroplasty (TSA). METHODS: All patients who underwent TSA from 2015 to 2017 were queried from the American College of Surgeons National Surgical Quality Improvement database. Patients were categorized based on preoperative hematocrit levels: normal (>39% for men and >36% for women), mild anemia (29% to 39% for men and 29% to 36% for women), and severe anemia (<29% for both men and women). RESULTS: A total of 10,547 patients were included in the study. Of these patients, 1,923 patients were (18.2%) in the mild anemia cohort and 146 (1.4%) were in the severe anemia cohort. Mild anemia was identified as a significant predictor of any complication (odds ratio [OR] 2.74, P < 0.001), stroke/cerebrovascular accident (OR 6.79, P = 0.007), postoperative anemia requiring transfusion (OR 6.58, P < 0.001), nonhome discharge (OR 1.79, P < 0.001), readmission (OR 1.63, P < 0.001), and return to the surgical room (OR 1.60, P = 0.017). Severe anemia was identified as a significant predictor of any complication (OR 4.31, P < 0.001), renal complication (OR 13.78, P < 0.001), postoperative anemia requiring transfusion (OR 5.62, P < 0.001), and nonhome discharge (OR 2.34, P < 0.001). CONCLUSION: Preoperative anemia status is a risk factor for complications within 30 days of TSA.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".