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Record W173650053

A sample of Canadian orthopedic surgeons expressed willingness to participate in osteoporosis management for fragility fracture patients.

2008· article· en· W173650053 on OpenAlexaffabout
Earl R. Bogoch, Elizabeth Snowden

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineOsteoporosisOrthopedic surgeryHip fractureFragilityPhysical therapyFragility fractureBone mineralSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The orthopedic community is in a unique position to initiate and provide osteoporosis care in fragility fracture patients to prevent future hip fractures in a high-risk population. The attitudes and intentions of Canadian orthopedic surgeons in the domain of osteoporosis care are unknown. Our objective was to identify current attitudes and osteoporosis management practices and to determine their overall willingness to participate in osteoporosis care for fragility fracture patients. METHODS: A real-time interactive polling session was conducted at the 58th Annual Meeting of the Canadian Orthopaedic Association. RESULTS: Of the orthopedic surgeons who responded, 90.4% agreed that the current emphasis on osteoporosis in orthopedic practice is appropriate; 85.2% of surgeons indicated that they currently refer or personally investigate for osteoporosis, or both, in fragility fracture patients. CONCLUSION: Most of the Canadian orthopedic surgeons sampled consider themselves to be currently engaged or ready to engage in osteoporosis care for fragility fracture patients. Focus should now shift from education and persuasion to program support through provision of resources and system modification that will enable Canadian orthopedic surgeons to effectively manage osteoporosis in their fracture patients.

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)0.000
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 score0.084
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.245
Teacher spread0.208 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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