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Record W3011632488 · doi:10.1111/jgs.16404

Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication

2020· article· en· W3011632488 on OpenAlexafffundabout
Robyn Tamblyn, David W. Bates, David L. Buckeridge, William G Dixon, Nadyne Girard, Jennifer S. Haas, Bettina Habib, Usman Iqbal, Jack Li, Thérèse Sheppard

Bibliographic record

VenueJournal of the American Geriatrics Society · 2020
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineHazard ratioAntidepressantInternal medicineConfidence intervalSertralineProportional hazards modelCitalopramRetrospective cohort studyMirtazapineDuloxetinePoison controlPsychiatryEmergency medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable populations, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications. DESIGN: Retrospective (2009-2014) cohort study. SETTING: Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan. PARTICIPANTS: Older antidepressant users-subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow-up. MEASUREMENTS: The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time-varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics. RESULTS: Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%-60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%-53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11-1.36 to HR = 1.43; 95% CI = 1.11-1.84) and sertraline (HR = 1.36; 95% CI = 1.10-1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06-1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00-1.86) and imipramine (HR = 1.16; 95% CI = 1.05-1.28), and atypicals (HR = 1.34; 95% CI = 1.14-1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice. CONCLUSION: The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494-1503, 2020.

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)0.008
metaresearch head score (Gemma)0.012
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 score0.021
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.275
Teacher spread0.261 · 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".

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Citations26
Published2020
Admission routes3
Has abstractyes

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