Impact of nursing home admission on health care use and disease status elderly dependent people one year before and one year after skilled nursing home admission based on 2012–2013 SNIIRAM data
Bibliographic record
Abstract
Abstract Background The aim of this study was to compare disease status and health care use 1 year before and 1 year after skilled nursing home (SNH) admission. Methods People over the age of 65 years admitted to SNH during the first quarter of 2013, covered by the national health insurance general scheme (69% of the population of this age), and still alive 1 year after admission were identified (n = 14,487, mean age: 86 years, women: 76%). Their reimbursed health care was extracted from the Système National d’Information Interrégimes de l’Assurance Maladie (SNIIRAM) [National Health Insurance Information System]. Results One year after nursing home admission, the most prevalent diseases were cardiovascular/neurovascular diseases and neurodegenerative diseases (affecting 45% and 40% of people before admission vs 51% and 53% after admission, respectively). Physical therapy use increased (43% vs 64% of people had at least one physical therapy session during the year, with an average of 47 vs 84 sessions/person during the year), while specialist consultations decreased (29% of people consulted an ophthalmologist at least once during the year before admission vs 25% after admission; 27% vs 21% consulted a cardiologist). Hospitalization rates were lower during the year following institutionalization (75% vs 40% of people were hospitalized at least once during the year), together with a lower emergency admission rate and a higher day admission rate. Conclusions Analysis of the new French reimbursement database specific to SNH shows that nursing home admission is associated with a reduction of some forms of outpatient care and hospitalizations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".