Development of core outcome sets for effectiveness trials of interventions to prevent and/or treat delirium (Del-COrS): study protocol
Bibliographic record
Abstract
INTRODUCTION: Delirium is a common, serious and potentially preventable condition with devastating impact on the quality of life prompting a proliferation of interventional trials. Core outcome sets aim to standardise outcome reporting by identifying outcomes perceived fundamental for measurement in trials of a specific interest area. Our aim is to develop international consensus on two core outcome sets for trials of interventions to prevent and/or treat delirium, irrespective of study population. We aim to identify additional core outcomes specific to the critically ill, acutely hospitalised patients, palliative care and older adults. METHODS AND ANALYSIS: We will conduct a systematic review of published and ongoing delirium trials (1980 onwards) and one-on-one interviews of patients who have experienced delirium and family members. These data will inform Delphi round 1 of a two-stage consensus process. In round 2, we will provide participants their own response, summarised group responses and those of patient/family participants for rescoring. We will randomise participants to receive feedback as proportion scoring the outcome as critical or as group mean responses. We will hold a consensus meeting using nominal group technique to finalise outcomes for inclusion. We will repeat the Delphi process and consensus meeting to select measures for each core outcome. We will recruit 240 Delphi participants giving us 80% power to detect a 1.0-1.5 point (9-point scale) difference by feedback method between rounds. We will analyse differences for subsequent scores, magnitude of opinion change, items retained and level of agreement. ETHICS AND DISSEMINATION: We are obtaining research ethics approvals according to local governance. Participation will be voluntary and data deidentified. Support from three international delirium organisations will be instrumental in dissemination and core outcome set uptake. We will disseminate through peer-reviewed open access publications and present at conferences selected to reach a wide range of knowledge users.
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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 | 0.162 | 0.193 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.069 | 0.019 |
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".