Reduced risk of suicidal behaviours associated with the treatment of hidradenitis suppurativa with tumour necrosis factor alpha antagonists: results from the <scp>US FDA</scp> Adverse Events Reporting System pharmacovigilance database
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, recurrent condition that presents as painful, suppurating lesions in the apocrine gland-bearing skin regions. HS has been associated with increased suicidal behaviours (SB), independent of any treatment. TNF-alpha antagonists are used to treat moderate-to-severe HS and have also been associated with SB, a factor that could confound the decision to use the TNF-α antagonists in the moderately to severely affected HS patients, who may already be experiencing increased SB risk. OBJECTIVES: To determine presence or absence of a safety signal for SB when HS is treated with TNF-α antagonists. METHODS: We calculated the reporting odds ratios (ROR) with 95% CI of SB associated with treatment for HS with TNF-α antagonists vs. the reference group of all other treatments for HS in the US Food and Drug Administration pharmacovigilance database from 1 January 2004 to 31 March 2019. A second analysis excluded isotretinoin (which has been used to treat HS and has also been associated with SB) from the reference group. RESULTS: There was a signal for decreased risk of SB with TNF-α antagonists (ROR = 0.1959, 95% CI 0.1247-0.3079; z = 7.071, P < 0.0001] vs. all other HS treatments; the ROR did not change significantly after isotretinoin was excluded from the reference group. CONCLUSIONS: Treatment of HS with TNF-α antagonists is associated with a decreased risk of SB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".