Personalized medical information card for adults with 22q11.2 deletion syndrome: An initiative to improve communication between patients and healthcare providers
Bibliographic record
Abstract
BACKGROUND: Many individuals with intellectual disabilities and their caregivers struggle to provide accurate and complete information to healthcare providers. METHOD: The present authors provided personal medical information cards (PMICs) containing contact and medical information to 52 Canadian adults with 22q11.2 deletion syndrome, a genetic condition associated with intellectual disability. The authors invited them and/or their caregivers to complete a user satisfaction survey concerning usage of the card. RESULTS: Forty-eight (92%) patients or their caregivers completed the survey. Twenty-two (46%) respondents used the PMIC over a median of 8 months during encounters with doctors and other professionals, and a majority of these used it more than once. Users reported finding the PMIC "very helpful" (86%) or "helpful" (14%), providing necessary information, speeding up interactions with professionals and helping avoid repeat storytelling. CONCLUSION: Providing a PMIC to individuals with intellectual disabilities and their caregivers could help improve patient safety and assist in advocacy.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".