Scratching the Surface: Itching for Evidence to Reduce Surgical Health Disparities in Total Shoulder Arthroplasty
Bibliographic record
Abstract
Total shoulder arthroplasty (TSA) is an effective procedure to improve symptoms, function, and quality of life for patients with different clinical conditions that affect the shoulder1,2. TSA use has been increasing in the United States3,4, but whether the short-term and longterm beneficial effects extend to all recipients is less clear. In this issue of The Journal , research by Singh and Cleveland5 adds to the growing body of literature evaluating the link between socioeconomic status (including insurance and income status) and postsurgical outcomes in patients with TSA. In this study, the authors conclude that public insurance, such as Medicaid and Medicare, were independently associated with more healthcare use (e.g., length of hospitalization and discharge to rehabilitation facilities) and suboptimal clinical outcomes, whereas lower income status was associated with less healthcare use and fewer postsurgical complications after TSA. The findings run counter to their hypothesis. The study offers clear advantages relative to previous research. First, the authors used the National Inpatient Sample (NIS), a nationally representative sample generalizable to all shoulder arthroplasties performed in the United States. This publicly available database with all-payer inpatient care data reduces the likelihood of selection bias that may occur in a single or multisite retrospective design6. The authors combined more than 15 years … Address correspondence to S.H. Liu, Division of Epidemiology, Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, 368 Plantation St., Worcester, Massachusetts 01605, USA. E-mail: shaohsien.liu{at}umassmed.edu
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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 | 0.060 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".