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Record W4406346651 · doi:10.3389/frhs.2024.1474195

Engaging older adults in diagnostic safety: implementing a diagnostic communication note sheet in a primary care setting

2025· article· en· W4406346651 on OpenAlexaff
Alberta Tran, Leah M. Blackall, Mary A. Hill, William J. Gallagher

Bibliographic record

VenueFrontiers in Health Services · 2025
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsToronto East General HospitalUniversity of Toronto
Fundersnot available
KeywordsPrimary careMedicineMedical emergencyPsychologyFamily medicine

Abstract

fetched live from OpenAlex

Introduction: Adults over the age of 65 are at a higher risk for diagnostic errors due to a myriad of reasons. In primary care settings, a large contributor of diagnostic errors are breakdowns in information gathering and synthesis throughout the patient-provider encounter. Diagnostic communication interventions, such as the Agency for Healthcare Research and Quality's "Be the Expert on You" note sheet, may require adaptations to address older adults' unique needs. Methods: = 6) in focus group sessions to understand their perspectives on diagnostic communication and the existing AHRQ note sheet. A two-page communication and clinic workflow tool was developed and implemented over a 6-month period using three Plan-Do-Check-Act cycles. Physicians, nurses, staff, and patients were surveyed. Results: = 31) found the tailored diagnostic communication note sheet to be easy-to-use, helpful for provider communication, and would recommend its use to other patients. Physicians and staff members were satisfied with the note sheet and described few challenges in using it in practice. Discussion: Our findings contribute to the growing body of evidence around diagnostic safety interventions and patient engagement by demonstrating the feasibility and benefits of actively involving older adult patients in quality initiatives.

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)0.002
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.317
Teacher spread0.311 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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