The Application of Medicare Data for Musculoskeletal Research in the United States: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal conditions disproportionately affect the lives of aging adults. We aimed to examine the literature using Medicare claims data in the United States for musculoskeletal surgical procedures. METHODS: Following the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, we searched the PubMed and Medline databases for peer-reviewed articles published between 1990 and 2015. We included the studies that (1) reported primary Medicare claims data use, (2) involved musculoskeletal surgery, and (3) were original peer-reviewed studies. We abstracted the types of surgical procedure and aims, and evaluated outcomes, and strengths and weaknesses of each included article. We assessed the quality of included articles with Newcastle Ottawa Assessment Scale. RESULTS: The literature search returned 3,233 articles, of which 119 met our inclusion criteria. These studies focused on different outcomes: epidemiology and treatment variation (26), cost of care (15), hospital-level analyses (30), health outcomes (31), the validity and accuracy of Medicare claims data (4), disparities in health care (10), and policy evaluation (3). DISCUSSION: Medicare claims data provide a unique way for researchers to study a nationally representative patient population longitudinally. A significant limitation of using claims data has been a lack of granularity on defining severity of a condition. LEVEL OF EVIDENCE: Therapeutic level III.
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 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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".