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Record W2023293941 · doi:10.2190/iq.29.3.c

High-Risk Patients' Readiness to Undergo BMD Testing for Osteoporosis Diagnosis in Pennsylvania

2009· article· en· W2023293941 on OpenAlexafffund
Jennifer M. Polinski, Suzanne M. Cadarette, Marilyn Arnold, Jeffrey N. Katz, Joel S. Finkelstein, M. Alan Brookhart, Claire Canning, Jerry Avorn, Daniel H. Solomon

Bibliographic record

VenueInternational Quarterly of Community Health Education · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchU.S. Public Health Service
KeywordsOsteoporosisMedicinePsychological interventionBone mineralPhysical therapyRandomized controlled trialSummative assessmentInternal medicinePsychologyFormative assessment

Abstract

fetched live from OpenAlex

The objective of this study was to better understand high-risk patients' readiness to engage in bone mineral density (BMD) testing to diagnose osteoporosis. Six hundred thirty-six participants in a randomized control trial for patients at high-risk for osteoporosis were surveyed. BMD screening readiness was measured by a three-item summative index. Multivariable linear regression examined the relationship between patients' scores on the index and constructs of osteoporosis and BMD testing knowledge, concern for developing osteoporosis and self-efficacy to engage in fall prevention behaviors. Participants had a mean age of 79 years, 96% were female and 80% were white. Greater concern for developing osteoporosis and better knowledge about BMD testing were significant predictors of a higher score on the index. Improving high-risk patients' knowledge about osteoporosis and the importance of BMD testing may enhance patients' readiness to undergo BMD testing. We found several correlates of readiness to undergo BMD screening that may be used to design effective interventions.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.728
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.128
GPT teacher head0.429
Teacher spread0.301 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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