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Record W2481513905 · doi:10.1053/j.ajkd.2016.06.005

Retraction Notice to “Kidney Function and Rate of Bone Loss at the Hip and Spine: The Canadian Multicentre Osteoporosis Study” [Am J Kidney Dis. 55(2):291-299]

2016· retraction· en· W2481513905 on OpenAlexaffabout
Sophie A. Jamal, Victoria Swan, Jacques P. Brown, David A. Hanley, Jerilynn C. Prior, Αλεξάνδρα Παπαϊωάννου, Lisa Langsetmo, Robert G. Josse

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2016
Typeretraction
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of British ColumbiaMcGill UniversityUniversity of CalgaryUniversité LavalMcMaster UniversityWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineNoticeSeriousnessLibrary scienceFamily medicineLawPolitical science

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: yes
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.006
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.087
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0380.031

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 head0.013
GPT teacher head0.302
Teacher spread0.290 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Not applicable
Domainnot available
GenreOther · Editorial

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

Citations1
Published2016
Admission routes2
Has abstractno

Explore more

Same venueAmerican Journal of Kidney Diseases→Same topicBone health and osteoporosis research→French-language works237,207→