MétaCan
Menu
Back to cohort
Record W4389220530 · doi:10.5770/cgj.26.683

Using Comprehensive Geriatric Assessment in Identifying Care Goals and Referral Services in a Frailty Intervention Clinic

2023· article· en· W4389220530 on OpenAlexaffvenueabout
Reenika Aggarwal, Suraj Brar, Michael S. Goodstadt, Rachel Devitt, Sara Penny, Meena Ramachandran, Danielle Underwood, Chloe Farand Taylor

Bibliographic record

VenueCanadian Geriatrics Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of TorontoProvidence Health Care
Fundersnot available
KeywordsMedicineReferralIntervention (counseling)RehabilitationHealth careGeriatricsPopulationFamily medicineGerontologyNursingPhysical therapyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

The proportion of older adults and frail adults in Canada is expected to rise significantly in upcoming years. Currently, a considerable number of older adults do not actively participate in developing their own care plans; prior research has indicated several benefits of patient engagement in this process. Thus, we conducted a mixed methods study that examined the prevalence of rehabilitation goals and identified these for 305 community dwelling older adults referred to a frailty intervention clinic utilizing Comprehensive Geriatric Assessment (CGA) between 2014 and 2018. Top patient concerns included mobility (84%), services, systems, and policies (51%), sensory functions and pain (50%), and self-care or domestic life (47%). The most common referrals or recommendations for patients included further follow-up with a physician or specialist (36%), referral to an onsite falls prevention clinic (31%), and medication modifications (31%). Based upon these findings, we recommend greater utilization of CGA within a team-based approach to improve patient care by allowing for greater collaboration and shared decision-making by health-care providers. Moreover, CGA can be an effective tool to meet the complex and unique health-care needs of frail patients while incorporating patient goals. This is vitally important considering the predicted growth in the population of frail and/or older patients, as well as the current challenges and shortfalls in meeting the health-care needs of this population.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.104
GPT teacher head0.394
Teacher spread0.290 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Geriatrics JournalSame topicFrailty in Older AdultsFrench-language works237,207