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Record W2292623230 · doi:10.1155/2016/2937426

Understanding Referral Patterns for Bone Mineral Density Testing among Family Physicians: A Qualitative Descriptive Study

2016· article· en· W2292623230 on OpenAlexaffabout
Sarah Munce, Sonya Allin, Leslie Carlin, Joanna E. M. Sale, Gillian Hawker, Sandra Kim, Debra A. Butt, Irene Polidoulis, Karen Tu, Susan Jaglal

Bibliographic record

VenueJournal of Osteoporosis · 2016
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsThe Scarborough HospitalWomen's College HospitalInstitute for Clinical Evaluative SciencesSt. Michael's HospitalToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineReferralCLARITYFamily medicineOsteoporosisThematic analysisQualitative researchDescriptive statisticsPopulationTest (biology)Physical therapyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Introduction. Evidence of inappropriate bone mineral density (BMD) testing has been identified in terms of overtesting in low risk women and undertesting among patients at high risk. In light of these phenomena, the objective of this study was to understand the referral patterns for BMD testing among Ontario's family physicians (FPs). Methods. A qualitative descriptive approach was adopted. Twenty-two FPs took part in a semi-structured interview lasting approximately 30 minutes. An inductive thematic analysis was performed on the transcribed data in order to understand the referral patterns for BMD testing. Results. We identified a lack of clarity about screening for osteoporosis with a tendency for baseline BMD testing in healthy, postmenopausal women and a lack of clarity on the appropriate age for screening for men in particular. A lack of clarity on appropriate intervals for follow-up testing was also described. Conclusions. These findings lend support to what has been documented at the population level suggesting a tendency among FPs to refer menopausal women (at low risk). Emphasis on referral of high-risk groups as well as men and further clarification and education on the appropriate intervals for follow-up testing is warranted.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.237
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.295
GPT teacher head0.404
Teacher spread0.109 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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