Frailty and pain in an acute private hospital: an observational point prevalence study
Bibliographic record
Abstract
Frailty and pain in hospitalised patients are associated with adverse clinical outcomes. However, there is limited data on the associations between frailty and pain in this group of patients. Understanding the prevalence, distribution and interaction of frailty and pain in hospitals will help to determine the magnitude of this association and assist health care professionals to target interventions and develop resources to improve patient outcomes. This study reports the point prevalence concurrence of frailty and pain in adult patients in an acute hospital. A point prevalence, observational study of frailty and pain was conducted. All adult inpatients (excluding high dependency units) at an acute, private, 860-bed metropolitan hospital were eligible to participate. Frailty was assessed using the self-report modified Reported Edmonton Frail Scale. Current pain and worst pain in the last 24 h were self-reported using the standard 0-10 numeric rating scale. Pain scores were categorised by severity (none, mild, moderate, severe). Demographic and clinical information including admitting services (medical, mental health, rehabilitation, surgical) were collected. The STROBE checklist was followed. Data were collected from 251 participants (54.9% of eligible). The prevalence of frailty was 26.7%, prevalence of current pain was 68.1% and prevalence of pain in the last 24 h was 81.3%. After adjusting for age, sex, admitting service and pain severity, admitting services medical (AOR: 13.5 95% CI 5.7-32.8), mental health (AOR: 6.3, 95% CI 1. 9-20.9) and rehabilitation (AOR: 8.1, 95% CI 2.4-37.1) and moderate pain (AOR: 3.9, 95% CI 1. 6-9.8) were associated with increased frailty. The number of older patients identified in this study who were frail has implications for managing this group in a hospital setting. This indicates a need to focus on developing strategies including frailty assessment on admission, and the development of interventions to meet the care needs of these patients. The findings also highlight the need for increased pain assessment, particularly in those who are frail, for more effective pain management.Trial registration: The study was prospectively registered (ACTRN12620000904976; 14th September 2020).
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".