The Residential History File: Studying Nursing Home Residents' Long‐Term Care Histories<sup>*</sup>
Bibliographic record
Abstract
OBJECTIVE: To construct a data tool, the Residential History File (RHF), that summarizes information from Medicare claims and nursing home (NH) Minimum Data Set (MDS) assessments to track people through health care locations, including non-Medicare-paid NH stays. DATA SOURCES: Online Survey of Certification and Reporting (OSCAR) data for 202 free-standing NHs, Medicare Denominator, claims (parts A and B), and MDS assessments for 60,984 people who were present in one of these NHs in 2006. METHODS: The algorithm creating the RHF is outlined and the RHF for the study data are used to describe place of death. The identification of residents in NHs is compared with the reports in OSCAR and part B claims. PRINCIPAL FINDINGS: The RHF correctly identified 84.8 percent of part B claims with place-of-service in NH, and it identified 18.3 less residents on average than reported in the OSCAR on the day of the survey. The RHF indicated that 17.5 percent non-Medicare NH decedents were transferred to the hospital to die versus 45.6 percent skilled nursing facility decedents. CONCLUSIONS: The population-based design of the RHF makes it possible to conduct policy-relevant research to examine the variation in the rate and type of health care transitions across the United States.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".