Increased use of psychiatric medication following incident celiac autoimmunity
Bibliographic record
Abstract
BACKGROUND: Celiac disease is associated with an increased risk of psychiatric disorders, yet little is known about formal treatment for such conditions among patients. METHODS: Using administrative healthcare data, we conducted a population-based cohort study of individuals newly positive for celiac autoimmunity (tTG) between 2015 and 2023 in Alberta, Canada. Individuals were linked to medication dispensations to assess use of antidepressants, anxiolytics, and antipsychotics. We calculated the proportion of days covered (PDC) by psychiatric medication as the number of days for which medication was prescribed. We conducted identical sub-analyses for individuals with anxiety and/or depression-related healthcare encounters prior to their positive tTG test. RESULTS: Among 14,323 newly tTG-positive individuals, a greater proportion dispensed antidepressants (22.5 % vs 27.6 %) and antipsychotics (4.6 % vs 6.0 %) post-tTG, while a lower proportion dispensed anxiolytics post-tTG (15.4 % vs 11.4 %), all p < 0.001. We observed higher odds of being dispensed psychiatric medication post-tTG (OR = 1.22; 95 % CI = 1.14, 1.31). Median PDC also significantly increased post-tTG test for antidepressants (2.8 % vs 21.1 %) and antipsychotics (0.4 % vs 6.3 %), p < 0.001. However, a decreased odds of medication dispensation (OR = 0.42; 95 % CI: 0.32, 0.56) and median PDC post-tTG (45.1 % vs. 10.1 %, p < 0.001) were observed among those with a previous depression/anxiety-related healthcare encounter. CONCLUSION: These findings highlight a need for increased screening for and/or monitoring of psychiatric comorbidities in the celiac population, and further investigation into what may be underpinning greater use of psychiatric medications after tTG positivity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".