The Effect of Age in the Association Between Frailty and Poor Sleep Quality in Community-Dwellers (The Atahualpa Project) (I9.009)
Bibliographic record
Abstract
Objective: We aimed to assess the effect of age in the association between poor sleep quality and frailty status. Background: The link between frailty and sleep disorders is plausible since both conditions are associated with enhanced expression of inflammatory biomarkers and dysfunction of the hypothalamic-pituitary-adrenocortical axis. Studies assessing frailty and disturbed sleep have used age as a confounding variable for adjustment in regression models, but there is no information on the effect of increasing age in this relationship. Design/Methods: Out of 351 Atahualpa residents aged ≥60 years identified during a door-to-door survey, 311 (89[percnt]) were interviewed with the Pittsburg Sleep Quality Index (PSQI) and the Edmonton Frail Scale (EFS). The independent association between PSQI and EFS scores was evaluated by the use of a generalized linear model adjusted for relevant confounders. A contour plot with Shepard interpolation was constructed to assess the effect of age in this association. Results: Mean score in the PSQI was 5 ± 2 points, with 34[percnt] individuals classified as poor sleepers. Mean score in the EFS was 5 ± 3 points, with 46[percnt] individuals classified as robust, 23[percnt] as pre-frail, and 31[percnt] as frail. In the fully adjusted model, higher scores in the PSQI were significantly associated with higher scores in the EFS (β: 0.23; 95[percnt] C.I.: 0.11-0.35; p<0.0001). Several clusters depicted the strong effect of age in the association between PSQI and EFS scores. Older individuals were more likely to be pre-frail or frail and poor sleepers, and younger individuals were more often robust and good sleepers. Clusters of younger individuals who were poor sleepers and frail accounted for the independent association between PSQI and EFS scores. Conclusions: This study shows the strong effect of age in the association between poor sleep quality and frailty status.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".