Feasibility of multimedia animations as preoperative guides for urgent abdominal surgeries in a public hospital in Brazil
Bibliographic record
Abstract
Health literacy, culture and language play vital roles in patients' understanding of health issues. Obstacles are more evident in low- and middle-income countries (LMICs), where inadequate patient education levels are higher and hospital resources are lower. This is a prospective pilot study assessing the feasibility of digital preoperative animations as guides for surgical patients. Patients admitted to a public hospital in Brazil for acute cholecystitis or appendicitis were included. Feasibility was represented by acceptability rate and ease of integration with department protocols. Thirty-four patients were included, and 26 patients concluded the intervention (76.5% acceptability rate). Demographic factors seemed to affect the results, indicated by higher acceptability from those with lower education levels, from younger patients and from women. Few studies have evaluated the use of multimedia resources for surgical patients, and no studies assessed the use of animations as digital patient education resources in an LMIC. This study demonstrated that the use of animations for patient education in LMICs is feasible. A step-based approach is proposed to aid the implementation of patient education digital interventions. The use of digital multimedia animations as preoperative guides in LMICs is feasible. It may help improve patient education and promote clinical benefits.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".