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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".