MétaCan
Menu
Back to cohort
Record W2738136796 · doi:10.1016/s0140-6736(17)31613-6

Romosozumab (sclerostin monoclonal antibody) versus teriparatide in postmenopausal women with osteoporosis transitioning from oral bisphosphonate therapy: a randomised, open-label, phase 3 trial

2017· article· en· W2738136796 on OpenAlexaff
Bente Langdahl, Cesar Libanati, Daria B. Crittenden, Michael A. Bolognese, Jacques P. Brown, Nadia Daizadeh, Eva Dokoupilová, Klaus Engelke, Joel S. Finkelstein, Harry K. Genant, Stefan Goemaere, Lars Hyldstrup, Esteban Jódar-Gimeno, Tony M. Keaveny, David L. Kendler, Péter Lakatos, Judy Maddox, Jorge Malouf, Fabio Massari, José Fernando Molina, María Rosa Ulla, Andreas Grauer

Bibliographic record

VenueThe Lancet · 2017
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of British ColumbiaCentre hospitalier de l'Université LavalUniversité Laval
Fundersnot available
KeywordsTeriparatideSclerostinMedicineOsteoporosisOpen labelInternal medicinePostmenopausal osteoporosisDenosumabOncologyMonoclonal antibodyBisphosphonateAntibodyClinical trialImmunologyBone mineralWnt signaling pathway

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)0.002
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.127
GPT teacher head0.420
Teacher spread0.293 · 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

Citations467
Published2017
Admission routes1
Has abstractno

Explore more

Same venueThe LancetSame topicBone health and osteoporosis researchFrench-language works237,207