Movement Is Life—Optimizing Patient Access to Total Joint Arthroplasty: Anemia and Sickle Cell Disease Disparities
Bibliographic record
Abstract
Anemia and sickle cell anemia before surgery are often unrecognized medical comorbidities that can and should be addressed. Nearly 6% of the American population meets the criteria for anemia. The elderly, along with patients with renal disease, cancer, heart failure, or diabetes mellitus are more likely to be anemic. The most common form of anemia is due to iron deficiency, which can be easily treated before surgery. Sickle cell anemia occurs in 1 in 365 Black births and 1 in 16,300 Hispanic births, with 100,000 Americans currently living with sickle cell anemia. Patients who have anemia or sickle cell anemia are at increased risk for postoperative complications, including the need for blood transfusions and delayed healing. For those with sickle cell disease, surgeries can precipitate a sickle cell crisis. Patients with sickle cell anemia face barriers in accessing appropriate care; however, these patients can be optimized using preoperative red blood cell transfusions to dilute sickle cells and elevate the hemoglobin level. There should also be careful consideration and monitoring of the pain level of patients with sickle cell anemia in the perioperative period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".