Use of Screening Tools for Frailty and Sarcopenia amongst Older Persons in Medical Outpatient Clinics to Facilitate Care Integration
Bibliographic record
Abstract
Objectives: The importance of screening for frailty and sarcopenia has grown. They have both been shown to be associated with disability, mortality and poor healthcare outcomes. With increasing subspecialisation in tertiary healthcare institutions, at risk older adults often receive fragmented care from organ-specific subspecialists. Several tools to do so exist and this study aimed to examine if the SARC-F and Edmonton frail screening tools are useful in clinical practice to identify at risk patients for intervention.Methods: This is a cross-sectional study of patients attending medical specialist outpatient clinics at the National University Hospital, Singapore from May 2015 to August 2016. Frailty and sarcopenia were identified using the Edmonton Frail Scale and SARC-F questionnaires respectively. Other clinically relevant data including basic demographics, presence of caregiver, number of follow-ups, medications and hospital readmissions in the past 1 year, Charlson’s comorbidity index and their Modified Barthel’s Index were collected.Results: A total of 115 patients 65years old and above were screened. The mean age of all patients was 76.6±6.5 years. 52.2% were female and 75.7% (n=87) were of Chinese ethnicity. 50% (n=57) of patients were independent and did not require a caregiver. Of the sample,44% (n=51) of patients were sarcopenic while 27% (n=31) were classified as frail. 23% (n=27) were both frail and sarcopenic. Women were more likely to be frail (67.7% vs 32.3%, p=0.042) and sarcopenic (58.3% vs 29.0%, p=0.001).Sarcopenic patients had a higher Charlson Comorbidities Index (5.0 vs 6.6, p=0.001) and lower modified Barthel’s Index (33 vs 78, p=0.001). Being sarcopenic was associated with a higher likelihood of having a caregiver (p=0.001) with an increasing dependence on children and domestic helpers. They had an average of 3.0 medical specialty follow ups compared to 2.3 follow ups for non-sarcopenic patients (p=0.004). Sarcopenia was significantly associated with polypharmacy (74.5% vs 42.1%, p=0.001), more than 2 hospital readmissions within a year (23.5% vs 9.4%, p=0.043), a higher number of falls (1.20 vs 0.17, p<0.001) and falls with significant consequences (0.14 vs. 0.02, p<0.001).Frail patients similarly had a higher Charlson Comorbidities Index (6.7 vs 5.3, p=0.013) and lower Modified Barthel’s Index (78 vs 97, p<0.001). They had 2.9 vs 2.1 specialty follow ups (p = 0.032). Frailty is associated with polypharmacy (87.1% vs. 45.2%, p<0.001), more than 2 hospital readmissions yearly (66.7% vs 33.3%, p<0.001), a higher number of falls (1.39 vs 0.35, p=0.001) and falls with significant consequences (0.16 vs 0.04, p=0.019).Conclusions: The prevalence of frailty and sarcopenia among elderly patients is high. Both syndromes are predictors of recurrent hospital admissions, polypharmacy, multiple medical clinic appointments, higher rate of falls and falls with serious consequences. Using simple screening tools to identify such at risk elderly to facilitate streamlining of care and care integration is likely to be beneficial and cost effective in the long run.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".