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Record W2944823603 · doi:10.1177/2333721419848153

Identification of Frailty in Primary Care: Feasibility and Acceptability of Recommended Case Finding Tools Within a Primary Care Integrated Seniors’ Program

2019· article· en· W2944823603 on OpenAlexaffabout
Marjan Abbasi, Sheny Khera, Julia Dabravolskaj, Melanie Garrison, Sharla King

Bibliographic record

VenueGerontology and Geriatric Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineTest (biology)AutonomyFocus groupPrimary careHealth carePopulationMedical emergencyFamily medicineGerontologyNursing

Abstract

fetched live from OpenAlex

Background: Case finding for frailty is recommended as part of routine clinical practice. We aimed to test feasibility and acceptability of three recommended case finding tools in primary care as part of an integrated seniors’ program. Method: Program of Research to Integrate Services for the Maintenance of Autonomy-7 (PRISMA-7), 4-m walk test, and electronic frailty index (eFI) were used as frailty case finding tools for a target population of community-dwelling seniors ≥65 years of age enrolled in a seniors’ program within an academic primary care clinic in Alberta, Canada. Feasibility was measured by percent completion rate and requirements for training/equipment/space/time, and acceptability by health care providers was measured using focus groups. Results: Eighty-five patients underwent case finding and 16 health care providers participated in the focus groups. Completion rate for PRISMA-7, 4-m walk test, and eFI was 97.6%, 93%, and 100%, respectively. No special training or equipment was required for PRISMA-7; brief training, equipment, and space were required for 4-m walk test. Both tools took less than 5 min to complete. Despite eFI requiring 10 to 20 min/patient chart, providers found it less intrusive. Conclusion: Despite feasibility of the tests, acceptance was higher for tools with minimal clinic interruption, low requirements for resources, and those with added benefit.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.047
GPT teacher head0.332
Teacher spread0.285 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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