Profile of elderly people hospitalized in general intensive care units in Rio Grande, southern Brazil: results of a cross-sectional survey
Bibliographic record
Abstract
At the end of the first quarter of this century, Brazil will have the sixth largest population for people aged 60 years or more worldwide. This will increase the demands on health services for this sector. This study aimed at assessing the profile of patients in this age group who were admitted to intensive care units (ICU) in the city of Rio Grande, Southern Brazil. A cross-sectional survey was carried out between April/2007 and March/2008 in the two local hospitals. Family members answered a standardized questionnaire that collected data about demographic and socioeconomic characte- ristics, household conditions, use of health services and current clinical conditions. Among the 213 elderly people included in this study, 90% came from Rio Grande, were married, aged 70 years or more, had at least five years of schooling, earned two or more minimum wages, were owners of their hou- se and did not have private health insuran- ce; 88% had a medical appointment in the previous six months and 56% were admitted to a hospital in the previous 12 months; half of them were unconscious when they were admitted in the ICU; three quarters of them needed mechanical ventilation and 45% died within the first eight days after hospital admission. This study identified some socioeconomic and environmental characteristics and health care needed by elderly people admitted to the ICU. This information can be used to set up preventive programs and to promote adequate clinical management among this population.
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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 | 0.000 | 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) | 0.001 | 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".