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Record W2885018923 · doi:10.1002/jbmr.3557

Comparing Strategies Targeting Osteoporosis to Prevent Fractures After an Upper Extremity Fracture (C-STOP Trial): A Randomized Controlled Trial

2018· article· en· W2885018923 on OpenAlexafffundabout
Sumit R. Majumdar, Finlay A. McAlister, Jeffrey Johnson, Brian H. Rowe, Debbie Bellerose, Imran Hassan, Douglas A. Lier, Stephanie Li, Walter P. Maksymowych, Matthew Rg Menon, Anthony S. Russell, B. Wirzba, Lauren A Beaupré

Bibliographic record

VenueJournal of Bone and Mineral Research · 2018
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesUniversity of Alberta
KeywordsMedicineNumber needed to treatRandomized controlled trialBisphosphonateOsteoporosisRandomizationConfidence intervalPhysical therapyBone mineralRelative riskAbsolute risk reductionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

We compared osteoporosis care after upper extremity fragility fracture using a low-intensity Fracture Liaison Service (FLS) versus a high-intensity FLS in a pragmatic patient-level parallel-arm comparative effectiveness trial undertaken at a Canadian academic hospital. A low-intensity FLS (active-control) that identified patients and notified primary care providers was compared to a high-intensity FLS (case manager) where a specially-trained nurse identified patients, investigated bone health, and initiated appropriate treatment. A total of 361 community-dwelling participants 50 years or older with upper extremity fractures who were not on bisphosphonate treatment were included; 350 (97%) participants completed 6-month follow-up undertaken by assessors blinded to group allocation. The primary outcome was difference in bisphosphonate treatment between groups 6 months postfracture; secondary outcomes included differences in bone mineral density (BMD) testing and a predefined composite measure termed "appropriate care" (taking or making an informed decision to decline medication for those with low BMD; not taking bisphosphonate treatment for those with normal BMD). Absolute differences (%), relative risks (RR with 95% confidence intervals [CIs]), number-needed-to-treat (NNT), and direct costs were compared. A total of 181 participants were randomized to active-control and 180 to case-manager using computer-generated randomization; the groups were similar on study entry. At 6 months, 51 (28%) active-control versus 86 (48%) case-manager participants started bisphosphonate treatment (20% absolute difference; RR 1.70; 95% CI, 1.28 to 2.24; p < 0.0001; NNT = 5). Of active-controls, 108 (62%) underwent BMD testing compared to 128 (73%) case-managed patients (11% absolute difference; RR 1.17; 95% CI, 1.01 to 1.36; p = 0.03). Appropriate care was received by 76 (44%) active-controls and 133 (76%) case-managed participants (32% absolute difference; RR 1.73; 95% CI, 1.43 to 2.09; p < 0.0001). The direct cost per participant was $18 Canadian (CDN) for the active-control intervention compared to $66 CDN for the case-manager intervention. In summary, case-management led to substantially greater improvements in bisphosphonate treatment and appropriate care within 6 months of fracture than the active control. © 2018 American Society for Bone and Mineral Research.

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 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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.049
GPT teacher head0.415
Teacher spread0.365 · 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 designRandomized trial
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

Citations30
Published2018
Admission routes3
Has abstractyes

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