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Record W2033505947 · doi:10.1007/s00198-014-2652-2

Too Fit To Fracture: a consensus on future research priorities in osteoporosis and exercise

2014· article· en· W2033505947 on OpenAlexafffund
Lora Giangregorio, Norma J. MacIntyre, Ari Heinonen, Angela M. Cheung, John D. Wark, Kathy M. Shipp, Stuart M. McGill, Maureen C. Ashe, Judi Laprade, Heather Keller, Αλεξάνδρα Παπαϊωάννου

Bibliographic record

VenueOsteoporosis International · 2014
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthUniversity of British ColumbiaUniversity Health NetworkUniversity of TorontoOsteoporosis CanadaMcMaster UniversityToronto Rehabilitation InstituteUniversity of Waterloo
FundersCanadian Institutes of Health ResearchCollege of Pharmacy and Nutrition, University of SaskatchewanState University of New York Upstate Medical UniversityOulun YliopistoJyväskylän YliopistoState University of New YorkUniversity of WaterlooResearch Institute for Aging, University of WaterlooUniversity of TorontoDeakin UniversityUniversity of New South WalesMcMaster UniversityOsteoporosis CanadaMcGill UniversityNeuroscience Research AustraliaHelsingin Yliopisto
KeywordsMedicinePsychological interventionOsteoporosisPhysical therapyFocus groupSports medicineKnowledge translationFamily medicineHip fractureStandardizationGerontologyNursingInternal medicine

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)0.075
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0050.006
Science and technology studies0.0060.017
Scholarly communication0.0150.021
Open science0.0070.011
Research integrity0.0210.033
Insufficient payload (model declined to judge)0.0080.002

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 head0.045
GPT teacher head0.380
Teacher spread0.335 · 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.

Study designQualitative
DomainMethods
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

Citations35
Published2014
Admission routes2
Has abstractno

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