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Record W7133027375

Trends in the use of raloxifene for osteoporosis, 1999 - 2022

2024· dissertation· W7133027375 on OpenAlexaffabout
Freddy Fares Shogry

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRaloxifeneFormularyOsteoporosisIncidence (geometry)EpidemiologyDrugSelective estrogen receptor modulator
DOInot available

Abstract

fetched live from OpenAlex

Raloxifene is a second-line therapy for postmenopausal osteoporosis, added to the Ontario Drug Benefit formulary under exceptional access in 1999/05. Coverage broadened in 2000/11, yet became more restrictive in 2003/04, and Health Canada issued safety warnings about fatal stroke in 2006/05. We aimed to describe older Ontario females initiating (incident) and taking (prevalent) raloxifene over time, 1999/05 – 2022/12. We identified 21,896 patients (mean age=74.2 years, SD=6.2; 5% fracture history, 57% bisphosphonate use). Raloxifene incidence initially increased, reaching a peak of 3,824 in 2001, followed by a gradual decline to 114 patients on average since 2015. In contrast, raloxifene prevalence remained over 5,000 annually between 2003 and 2011, with a gradual drop from 4,670 in 2012 to 959 in 2022. Patient characteristics were similar over time. Real-world evidence is needed to inform clinical decision making so raloxifene is considered among select patients where benefits in fracture reduction outweigh harms.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.494

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.092
GPT teacher head0.434
Teacher spread0.343 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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