Assessing Recovery from Delirium: An International Survey of Healthcare Professionals Involved in Delirium Care.
Bibliographic record
Abstract
Background: A crucial part of delirium care is determining if the delirium episode has resolved. Yet, there is no clear evidence or consensus on which assessments clinicians should use to assess for delirium recovery. Objective: To evaluate current opinions from delirium specialists on assessment of delirium recovery. Design: Online questionnaire-based survey distributed internationally to healthcare professionals involved in delirium care. Methods: The survey covered methods for assessing recovery, the importance of different symptom domains for capturing recovery, and local guidance or pathways that recommend monitoring for delirium recovery. Results: Responses from 199 clinicians were collected. Respondents were from the UK (51%), US (13%), Australia (9%), Canada (7%), Ireland (7%) and 16 other countries. Most respondents were doctors (52%) and nurses (27%). Clinicians worked mostly in geriatrics (52%), ICUs (21%) and acute assessment units (17%). Ninety-four percent of respondents indicated that they conduct repeat delirium assessments (i.e., on ≥2 occasions) to monitor delirium recovery. The symptom domains considered most important for capturing recovery were: arousal (92%), inattention (84%), motor disturbance (84%), and hallucinations and delusions (83%). The most used tool for assessing recovery was the 4 'A's Test (4AT, 51%), followed by the Confusion Assessment Method (CAM, 26%), the CAM for the ICU (CAM-ICU, 17%) and the Single Question in Delirium (SQiD, 11%). Twenty-eight percent used clinical features only. Less than half (45%) of clinicians reported having local guidance that recommends monitoring for delirium recovery. Conclusions: The survey results suggest a lack of standardisation regarding tools and methods used for repeat delirium assessment, despite consensus surrounding the key domains for capturing delirium recovery. These findings emphasise the need for further research to establish best practice for assessing delirium recovery.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".