Core outcome set for studies evaluating interventions to prevent or treat delirium in long-term care older residents: international key stakeholder informed consensus study
Bibliographic record
Abstract
BACKGROUND: Trials of interventions to prevent or treat delirium in older adults resident in long-term care settings (LTC) report heterogenous outcomes, hampering the identification of effective management strategies for this important condition. Our objective was to develop international consensus among key stakeholders for a core outcome set (COS) for future trials of interventions to prevent and/or treat delirium in this population. METHODS: We used a rigorous COS development process including qualitative interviews with family members and staff with experience of delirium in LTC; a modified two-round Delphi survey; and virtual consensus meetings using nominal group technique. The study was registered with the Core Outcome Measures in Effectiveness Trials (COMET) initiative (https://www.comet-initiative.org/studies/details/796). RESULTS: Item generation identified 22 delirium-specific outcomes and 32 other outcomes from 18 qualitative interviews. When combined with outcomes identified in our earlier systematic review, and following an item reduction step, this gave 43 outcomes that advanced to the formal consensus processes. These involved 169 participants from 12 countries, and included healthcare professionals (121, 72%), researchers (24, 14%), and family members/people with experience of delirium (24, 14%). Six outcomes were identified as essential to include in all trials of interventions for delirium in LTC, and were therefore included in the COS. These are: 'delirium occurrence'; 'delirium related distress'; 'delirium severity'; 'cognition including memory', 'admission to hospital' and 'mortality'. CONCLUSIONS: This COS, endorsed by the American Delirium Society and the European and Australasian Delirium Associations, is recommended for use in future clinical trials evaluating delirium prevention or treatment interventions for older adults residing in LTC.
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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 | 0.444 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".