Medical graphics to improve patient understanding and anxiety in elderly and cognitively impaired patients scheduled for transcatheter aortic valve implantation (TAVI)
Bibliographic record
Abstract
Background Anxiety and limited patient comprehension may pose significant barriers when informing elderly patients about complex procedures such as transcatheter aortic valve implantation (TAVI). Objectives We aimed to evaluate the utility of medical graphics to improve the patient informed consent (IC) before TAVI. Methods In this prospective, randomized dual center study, 301 patients were assigned to a patient brochure containing medical graphics (Comic group, n = 153) or sham information (Control group, n = 148) on top of usual IC. Primary outcomes were patient understanding of central IC-related aspects and periprocedural anxiety assessed by the validated Spielberger State Trait Anxiety Inventory (STAI), both analyzed by cognitive status according to the Montreal Cognitive Assessment (MoCA). Results Patient understanding was significantly higher in the Comic group [mean number of correct answers 12.8 (SD 1.2) vs. 11.3 (1.8); mean difference 1.5 (95% CI 1.2–1.8); p < 0.001]. This effect was more pronounced in the presence of cognitive dysfunction (MoCA < 26) [12.6 (1.2) in the Comic vs. 10.9 (1.6) in the Control group; mean difference 1.8 (1.4–2.2), p < 0.001]. Mean STAI score declined by 5.7 (95% CI 5.1–6.3; p < 0.001) in the Comic and 0.8 points (0.2–1.4; p = 0.015) in the Control group. Finally, mean STAI score decreased in the Comic group by 4.7 (3.8–5.6) in cognitively impaired patients and by 6.6 (95% CI 5.8 to 7.5) in patients with normal cognitive function ( p < 0.001 each). Conclusions Our results prove beneficial effects for using medical graphics to inform elderly patients about TAVI by improving patient understanding and reducing periprocedural anxiety (DRKS00021661; 23/Oct/2020).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".