A National Modified Delphi Consensus Process to Prioritize Experiences and Interventions for Antipsychotic Medication Deprescribing Among Adult Patients With Critical Illness
Bibliographic record
Abstract
Antipsychotic medications are frequently prescribed to critically ill patients leading to their continuation at transitions of care thereafter. The aim of this study was to generate evidence-informed consensus statements with key stakeholders on antipsychotic minimization and deprescribing for ICU patients. DESIGN: We completed three rounds of surveys in a National modified Delphi consensus process. During rounds 1 and 2, participants used a 9-point Likert scale (1-strongly disagree, 9-strongly agree) to rate perceptions related to antipsychotic prescribing (i.e., experiences regarding delivery of patient care), knowledge and frequency of antipsychotic use, knowledge surrounding antipsychotic guideline recommendations, and strategies (i.e., interventions addressing current antipsychotic prescribing practices) for antipsychotic minimization and deprescribing. Consensus was defined as a median score of 1-3 or 7-9. During round 3, participants ranked statements on antipsychotic minimization and deprescribing strategies that achieved consensus (median score 7-9) using a weighted ranking scale (0-100 points) to determine priority. SETTING: Online surveys distributed across Canada. SUBJECTS: Fifty-seven stakeholders (physicians, nurses, pharmacists) who work with ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Participants prioritized six consensus statements on strategies for consideration when developing and implementing interventions to guide antipsychotic minimization and deprescribing. Statements focused on limiting antipsychotic prescribing to patients: 1) with hyperactive delirium, 2) at risk to themselves, their family, and/or staff due to agitation, and 3) whose care and treatment are being impacted due to agitation or delirium, and prioritizing 4) communication among staff about antipsychotic effectiveness, 5) direct and efficient communication tools on antipsychotic deprescribing at transitions of care, and 6) medication reconciliation at transitions of care. CONCLUSIONS: We engaged diverse stakeholders to generate evidence-informed consensus statements regarding antipsychotic prescribing perceptions and practices that can be used to implement interventions to promote antipsychotic minimization and deprescribing strategies for ICU patients with and following critical illness.
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 | 0.155 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".