Abstract NS4: Challenges and Lessons Learned in an Ongoing Randomized Controlled Trial to Test the Effectiveness of a Multicomponent Intervention in Improving Delirium Outcomes in Acute Stroke
Bibliographic record
Abstract
Delirium in acute stroke has higher morbidity and mortality than those without delirium. This two-group randomized controlled trial (RCT) tests if a multicomponent intervention improves delirium outcomes in stroke patients at a comprehensive stroke center. This presentation describes the challenges in managing a RCT in acute stroke. Scientific rigor requires coordinating staff, enrolling adequate sample size, assuring intervention fidelity, and fostering data integrity. A sample of 282 subjects is required for 80% power (α=0.05) to determine a 10% reduction in incident delirium. Eligibility includes acute stroke, aged ≥ 50 years, no aphasia or delirium on admit. Subjects randomized to Usual Care (UC) or Delirium Care (DC). Both groups receive standardized Stroke Care. DC subjects receive a multicomponent intervention: 1) pharmacist recommendations using Anticholinergic Drug scale scores; and 2) therapeutic activities. NIHSS, Montreal Cognitive Assessment (MoCA), Confusion Assessment Method (CAM), and mRS are used to determine primary (delirium) and secondary endpoints (LOS, neurological deficit, functional status). A total of 513 patients screened over 289 consecutive days required unbudgeted staff. The 310 excluded [aphasia (94), baseline delirium (36), critically ill (92), LOS <2 days (69), other causes (19)] unexpectedly impacted enrollment. Of eligible, 66% (133/203) consented; UC (n=65), DC (n=68). Hospital volunteers engaged DC subjects in therapeutic activities twice daily, including holidays. Two pharmacists independently made recommendations for each group. Outcome data were validated at daily rounds. Stroke-related cognitive dysfunction required more time than norm to complete the MoCA. Both the CAM and team consensus was used to confirm delirium. Lessons learned: 1) study staff attending rounds was key to enrollment, intervention fidelity, and data integrity; 2) securing a pool of on-call volunteers to sustain therapeutic activities was required; and 3) additional and unanticipated resources were needed. In conclusion, successful conduct of a RCT in acute stroke patients requires a dedicated well-trained study staff 7 days a week, including holidays.
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 | 0.331 | 0.322 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".