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Record W2760045808 · doi:10.5770/cgj.20.284

Canadian Frailty Network (CFN) National Conference Abstracts

2017· article· en· W2760045808 on OpenAlexaffvenueabout
John Muscedere, Sarah Grace Bebenek, Denise Stockley, Laura Kinderman, Carol Barrie

Bibliographic record

VenueCanadian Geriatrics Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineWork (physics)Medical educationLibrary scienceGerontology

Abstract

fetched live from OpenAlex

One of the components of the Canadian Frailty Network (CFN) Interdisciplinary Training Program is for HQPs to disseminate their work through publication and meetings. The main dissemination event of CFN is the annual conference; the 3rd CFN Annual Conference on Improving Care for the Frail Elderly was held in Toronto, September 27-29, 2015 and the 4th CFN Annual Conference was held in Toronto, on April 23-24, 2017. The goal for both conferences was to bring together key researchers, practitioners, educators, policy-makers, advocates, and organizations devoted to improving health care for the seriously ill, frail elderly, and to highlight HQP research. All 2015 and 2016 HQPs in the Summer Student Award Program, the Interdisciplinary Fellowship Project, and project HQP involved with a CFN-funded project submitted an abstract for their affiliated conference. The abstracts were reviewed for quality, and the authors presented them as posters during the conferences. In what follows, we present the compilation of research abstracts that were presented by CFN HQP at the 3rd and 4th annual conferences. The annual conference will continue to be expanded in coming years, and next year we will accept abstracts from all researchers who are engaged with the seriously ill, frail elderly.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.291
Teacher spread0.244 · 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 designNot applicable
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

Citations0
Published2017
Admission routes3
Has abstractyes

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