Identifying a Set of Quality Indicators for Older Adults Hospitalized After Injury: An Expert Consensus Study
Bibliographic record
Abstract
BACKGROUND: Older people account for over 50% of trauma admissions and are at increased risk of adverse outcomes. Several authors have proposed quality indicators for geriatric trauma care. However, there is still no consensus on those that should be used to assess the quality of care in this population. This study aims to reach a consensus on a set of indicators for assessing the quality of care of older patients hospitalized for injury. METHODS: We conducted a consensus study using the RAND-UCLA Appropriateness method, using an individual online questionnaire (round 1) and a virtual group meeting (round 2). Forty indicators identified in the literature were submitted to a panel of experts in trauma and geriatrics and three patient partners from the inclusive trauma system of the province of Quebec (Canada). Indicators were evaluated using four criteria: importance, evidence, actionability, and measurability. Consensus was defined using RAND-UCLA criteria. RESULTS: 30/41 (73%) invited participants completed the two rounds. In the first round, 12 indicators were retained and 14 were rejected. In the second round, three additional indicators were retained. The final set consisted of 15 indicators, including early mobilization and rehabilitation, delirium screening, documentation of the level of care < 48 h, favorable discharge destination, optimal pain management using appropriate modalities for older people, and surgical delay. CONCLUSION: We propose a set of 15 quality indicators based on evidence, expert consensus, and patient partners' priorities that could be implemented in trauma systems and contribute to improving the quality of hospital care for older patients.
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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 | 0.326 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".