Frailty in Portuguese Older Patients From Convalescence Units: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Frailty is a common geriatric syndrome, associated with adverse clinical outcomes. Nevertheless, studies about frailty in continuous care units are scarce. In this way, this study aimed to assess frailty in older patients admitted in convalescence units (CUs) and analyze its association with demographic, social and clinical characteristics. Methods: This cross-sectional study included older patients admitted in eight CUs of the Integrated Continued Care National Network in Northern Portugal. Exclusion criteria were: total ≤ 11 in Glasgow coma scale, < 10 in mini-mental state examination or being unable to communicate. A comprehensive protocol was administered to assess health-related and lifestyle characteristics, comorbidity, dependence on activities of daily living (ADL), depressive and anxiety symptoms, cognition, and socio-familial risk. Frailty was assessed by Tilburg frailty indicator (TFI). Results: A sample of 165 patients was included (median age = 77; 65% female), with 80% classified as frail, mostly women (P = 0.002), widowed (P = 0.016), shorter (P = 0.005), feeling more tired (P < 0.005) and with less energy (P < 0.005). Also, these patients reported more vision problems (P = 0.006), difficulties in walking (P = 0.022) and climbing stairs (P = 0.029), pain (P = 0.004), falls (P = 0.046), non-alcohol use (P = 0.043) and non-physical activity (P = 0.032). Frail patients had a higher number of previous hospitalizations (P = 0.018), comorbidity (P = 0.006), dependence on instrumental (P < 0.001) and basic (P = 0.006; P < 0.001) ADL, depressive (P < 0.001) and anxiety (P = 0.002) symptoms. After adjusting for covariates, frailty was associated with females (adjusted odds ratio (aOR) = 4.45, P = 0.011), vascular disease (aOR = 4.40, P = 0.040), vision problems (aOR = 10.85, P < 0.001), high dependency on instrumental ADL (aOR = 0.74, P = 0.002), and depressive symptoms (aOR = 1.37, P = 0.001). Conclusions: Frailty is high among older patients in CUs, particularly in females, with vascular disease, vision problems, instrumental ADL dependence and depressive symptoms. Thus, frailty should be screened, and preventive and therapeutic measures should be considered for those at high risk, in order to minimize possible negative consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".