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Record W4284879790 · doi:10.1007/s11136-022-03175-2

Baseline health-related quality of life predicts falls: a secondary analysis of a randomized controlled trial

2022· article· en· W4284879790 on OpenAlexafffund
Jennifer C. Davis, Chun Liang Hsu, Cheyenne Ghag, Samantha Y. Starkey, Patrizio Jacova, Larry Dian, Naaz Parmar, Kenneth Madden, Teresa Liu‐Ambrose

Bibliographic record

VenueQuality of Life Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaVancouver Coastal Health
FundersCanadian Institutes of Health Research
KeywordsMedicineRandomized controlled trialQuality of life (healthcare)Physical therapyPoison controlConfidence intervalInjury preventionSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0110.010
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.212
GPT teacher head0.509
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes2
Has abstractno

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