Patterns of Hospitalization Risk for Women Surviving Into Very Old Age
Bibliographic record
Abstract
BACKGROUND: By 2050, adults aged 80 years and over will represent around 20% of the global population. Little is known about how adults surviving into very old age use hospital services over time. OBJECTIVE: The objective of the study was to examine patterns of hospital usage over a 10-year period for women who were aged 84 to 89 in 2010 and examine factors associated with increased use. METHODS: Survey data from 1936 women from the 1921 to 1926 cohort of the Australian Longitudinal Study on Women's Health were matched with the state-based Admitted Patients Data Collection. Hospital use profiles were determined using repeated measures latent class analysis. RESULTS: Four latent class trajectories were identified. One-quarter of the sample were at low risk of hospitalization, while 20.6% demonstrated increased risk of hospitalization and a further 38.1% had moderate hospitalization risk over time. Only 16.8% of the sample was classified as having high hospitalization risk. Correlates of hospital use for very old women differed according to hospital use class and were contingent on the timing of exposure (ie, short-term or long-term). CONCLUSIONS: Despite the perception that older adults place a significant burden on health care systems, the majority of women demonstrated relatively low hospital use over an extended period, even in the presence of chronic health conditions. High hospitalization risk was found to be concentrated among a small minority of these long-term survivors. The findings suggest the importance of service planning and treatment regimes that take account of the diverse trajectories of hospital use into and through advanced old age.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".