Incremento en la utilización de servicios hospitalarios por las personas mayores de 55 años: envejecimiento poblacional y respuesta del sistema de servicios de salud
Bibliographic record
Abstract
Examinar los cambios en la tasa de hospitalización entre 1985 y 1994 en España y en Andalucía, y estimar la proporción atribuible al envejecimiento poblacional. Se calcula el incremento de las tasas de hospitalización entre 1985 y 1994. Se estiman las altas esperadas en 1994 multiplicando las tasas de hospitalización de 1985 por la población en 1994 para cada grupo de edad. Entre 1985 y 1994 se produjo un incremento en la tasa de hospitalización en los mayores de 55 años. El incremento oscila entre el 20,01% (de 55 a 64 años) y el 52% (> 75 años); y el porcentaje atribuible al envejecimiento poblacional es pequeño (2,8–14,8%). El aumento en la tasa de hospitalización en mayores de 55 años se explica parcialmente por el envejecimiento poblacional, atribuible a la demanda y a la oferta de servicios. To examine changes in hospital service utilization between 1985 and 1994 in Spain and Andalucía and to estimate the proportion of change due to population ageing. The increase in hospitalization rates between 1985 and 1994 was calculated. Expected hospital discharges in 1994, were calculated by multiplying hospital discharge rates in 1985 by the population in 1994 for each age group. Between 1985 and 1994 hospitalization rates increased among persons aged 55 years old order. The increase ranged from 20.01% (55–64 years old) to 52% (more than 75 years old). The percentage of this increase due to population ageing was slight (range: 2.8 to 14.8%). The increase in hospitalization rates among persons aged 55 years old or older is only partially explained by population ageing, which can be attributed to supply and demand of services.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".