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Record W3024627260 · doi:10.1111/jar.12747

Personalized medical information card for adults with 22q11.2 deletion syndrome: An initiative to improve communication between patients and healthcare providers

2020· article· en· W3024627260 on OpenAlexafffundabout
Joanne C. Y. Loo, Erik Boot, Maria Corral, Anne S. Bassett

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2020
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoUniversity Health NetworkToronto General Hospital
FundersNational Institute of Mental HealthCanadian Institutes of Health Research
KeywordsIntellectual disabilityHealth professionalsHealth careMedicineFamily medicinePsychologyNursingMedical educationPsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.383
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes3
Has abstractyes

Explore more

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