100 SCREENING FOR SLEEP DISTURBANCE AS PART OF THE COMPREHENSIVE GERIATRIC ASSESSMENT
Bibliographic record
Abstract
Abstract Background Sleep disturbance is common in the older patient, and has been associated with many common comorbidities, falls, impaired quality of life, and mortality. Sleep disturbance screening and management are important elements of the comprehensive geriatric assessment (CGA). There are few sleep disturbance assessment tools, however, that have been validated for use in older patients, or in those with cognitive impairment. We sought to review the assessment and management of sleep disturbance within a regional integrated care outreach programme (ICOP), where the RU-SATED tool had been used. Methods From March–June’21 the notes of 30 consecutive patients, presenting to ICOP with cognitive complaints, were reviewed. Demographic and sleep disturbance data were collected, in addition to whether advice, treatment or further assessment for sleep disturbance was documented. Feedback from staff on the utility of the RU-SATED tool was also sought. Results Of those reviewed, the mean age was 80.3 (SD = 7.8), and the mean Montreal Cognitive Assessment score (MOCA) was 17.7 (SD = 5.9). Where data was available, the mean RU-SATED score was 7.6 (SD = 1.9), and 42% were frail (n = 11). The most commonly endorsed sleep symptom was sleep onset latency/sleep maintenance issues (n = 16). RU-SATED score did not significantly predict age, MOCA or frailty (p > 0.1 for all). Advice, treatment or further assessment of sleep disturbance was offered to 23% of patients (n = 7), and this was not predicted by overall RU-SATED score or by individual question responses (p > 0.1 for all). Staff also stated that they found elements of the tool difficult to explain and interpret. Conclusion Although the numbers are small, we have not found the RU-SATED tool to be helpful in guiding the management of sleep disturbance in our cohort. A more practical CGA tool is currently in development by the ICOP service.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.001 |
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".