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Investigation of Suicidality and Psychological Adverse Events in Patients Treated With Finasteride

2020· article· en· W3098715168 on OpenAlexaff
David‐Dan Nguyen, Maya Marchese, Eugene B. Cone, Marco Paciotti, Shehzad Basaria, Naeem Bhojani, Quoc‐Dien Trinh

Bibliographic record

VenueJAMA Dermatology · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill University
FundersNational Institute on Aging
KeywordsFinasterideMedicineAdverse effectDutasterideMale-pattern baldnessTamsulosinPharmacovigilanceAnxietyPsychiatryInternal medicineHyperplasiaProstate cancerDermatologyCancerProstate

Abstract

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Importance: There is ongoing controversy about the adverse events of finasteride, a drug used in the management of alopecia and benign prostatic hyperplasia (BPH). In 2012, reports started emerging on men who had used finasteride and either attempted or completed suicide. Objective: To investigate the association of suicidality (ideation, attempt, and completed suicide) and psychological adverse events (depression and anxiety) with finasteride use. Design, Setting, and Participants: This pharmacovigilance case-noncase study used disproportionality analysis (case-noncase design) to detect signals of adverse reaction of interest reported with finasteride in VigiBase, the World Health Organization's global database of individual case safety reports. To explore the strength of association, the reporting odds ratio (ROR), a surrogate measure of association used in disproportionality analysis, was used. Extensive sensitivity analyses included stratifying by indication (BPH and alopecia) and age (≤45 and >45 years); comparing finasteride signals with those of drugs with different mechanisms but used for similar indications (minoxidil for alopecia and tamsulosin hydrochloride for BPH); comparing finasteride with a drug with a similar mechanism of action and adverse event profile (dutasteride); and comparing reports of suicidality before and after 2012. Data were obtained in June 2019 and analyzed from January 25 to February 28, 2020. Exposures: Reported finasteride use. Main Outcomes and Measures: Suicidality and psychological adverse events. Results: VigiBase contained 356 reports of suicidality and 2926 reports of psychological adverse events (total of 3282 adverse events of interest) in finasteride users (3206 male [98.9%]; 615 of 868 [70.9%] with data available aged 18-44 years). A significant disproportionality signal for suicidality (ROR, 1.63; 95% CI, 1.47-1.81) and psychological adverse events (ROR, 4.33; 95% CI, 4.17-4.49) in finasteride was identified. In sensitivity analyses, younger patients (ROR, 3.47; 95% CI, 2.90-4.15) and those with alopecia (ROR, 2.06; 95% CI, 1.81-2.34) had significant disproportionality signals for increased suicidality; such signals were not detected in older patients with BPH. Sensitivity analyses also showed that the reports of these adverse events significantly increased after 2012 (ROR, 2.13; 95% CI, 1.91-2.39). Conclusions and Relevance: In this pharmacovigilance case-noncase study, significant RORs of suicidality and psychological adverse events were associated with finasteride use in patients younger than 45 years who used finasteride for alopecia. The sensitivity analyses suggest that these disproportional signals of adverse events may be due to stimulated reporting and/or younger patients being more vulnerable to finasteride's adverse effects.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.310
Teacher spread0.254 · 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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Citations115
Published2020
Admission routes1
Has abstractyes

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