Aggregate Health Burden and the Risk of Hospitalization in Older Persons Post Hip Replacement Surgery
Bibliographic record
Abstract
BACKGROUND: We sought to understand the association between aggregate health burden-chronic conditions, functionally limiting health problems and mental well-being-and the likelihood of hospitalization among older persons post hip replacement surgery. METHODS: Eight hundred and twenty-eight Medicare recipients from three U.S. states completed a questionnaire 3 years postsurgery. Using administrative data (Medicare Provider Analysis and Review), participants were prospectively followed for 12 months postquestionnaire to capture hospitalizations. Using logistic regression, demographic, socioeconomic, and behavioral characteristics and medical comorbidities were considered as predictors. Subsequently, musculoskeletal (MSK) functional and geriatric problems were added as predictors, then mental well-being and activity limitations. Path analysis was employed to elucidate interrelationships between these predictors, investigating whether mediated effects through mental well-being and activity limitations were operational. RESULTS: Mean age was 76 years (range: 67-96); 63% were women; 23% had ≥1 hospitalization(s). When medical comorbidity, MSK limitations, and geriatric problems were considered, each was independently associated with hospitalization (odds ratios: 1.3, 1.1, 1.2, respectively). When mental well-being and activity limitations were added, these variables were predictive of hospitalization (odds ratios: 1.2, 1.1, respectively), while MSK limitations and geriatric problems were no longer predictive. Path analysis results suggested that the influence of medical comorbidity and MSK and geriatric problems were mediated through mental well-being and activity limitations. CONCLUSIONS: Several health domains predict hospitalization, beyond and including medical comorbidity. Efforts aimed at delaying/minimizing hospitalizations in this population should consider an array of domains for potentially targeted intervention. These findings can serve as a baseline against which future research can assess the impact of changes to the health care system.
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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 | 0.004 |
| 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 | 0.001 |
| 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".