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Record W2836468119 · doi:10.5770/cgj.21.296

Towards Consensus on Essential Components of Physical Examination in Primary Care-based Memory Clinics*

2018· article· en· W2836468119 on OpenAlexafffundvenueabout
George Heckman, Bryan B. Franco, Linda Lee, Loretta M. Hillier, Véronique Boscart, Paul Stolee, Lauren Crutchlow, Joel A. Dubin, Frank Molnar, Dallas Seitz

Bibliographic record

VenueCanadian Geriatrics Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBruyèreOttawa HospitalUniversity of OttawaSt Joseph's Health CareQueen's UniversityMcMaster UniversityParkwood InstituteWestern UniversityConestoga CollegeResearch Institute for AgingUniversity of Waterloo
FundersAlzheimer Society
KeywordsMedicineMemory clinicContext (archaeology)DementiaPhysical examinationPrimary careNursingFamily medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Primary care-based memory clinics were established to meet the needs of persons with memory concerns. We aimed to identify: 1) physical examination maneuvers required to assess persons with possible dementia in specialist-supported primary care-based memory clinics, and 2) the best-suited clinicians to perform these maneuvers in this setting. METHODS: We distributed in-person and online surveys of clinicians in a network of 67 primary care-based memory clinics in Ontario, Canada. RESULTS: 90 surveys were completed for an overall response rate of 66.7%. Assessments of vital signs, gait, and for features of Parkinsonism were identified as essential by most respondents. There was little consensus on which clinician should be responsible for specific physical examination maneuvers. CONCLUSIONS: While we identified specific physical examination maneuvers deemed by providers to be both necessary and feasible to perform in the context of primary care-based memory clinics, further research is needed to clarify interprofessional roles related to the examination.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.311
Teacher spread0.288 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2018
Admission routes4
Has abstractyes

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