Incidence and temporal trends of co-occurring personality disorder diagnoses in immune-mediated inflammatory diseases
Bibliographic record
Abstract
AIMS: Although immune-mediated inflammatory diseases (IMID) are associated with multiple mental health conditions, there is a paucity of literature assessing personality disorders (PDs) in these populations. We aimed to estimate and compare the incidence of any PD in IMID and matched cohorts over time, and identify sociodemographic characteristics associated with the incidence of PD. METHODS: We used population-based administrative data from Manitoba, Canada to identify persons with incident inflammatory bowel disease (IBD), multiple sclerosis (MS) and rheumatoid arthritis (RA) using validated case definitions. Unaffected controls were matched 5:1 on sex, age and region of residence. PDs were identified using hospitalisation or physician claims. We used unadjusted and covariate-adjusted negative binomial regression to compare the incidence of PDs between the IMID and matched cohorts. RESULTS: We identified 19 572 incident cases of IMID (IBD n = 6,119, MS n = 3,514, RA n = 10 206) and 97 727 matches overall. After covariate adjustment, the IMID cohort had an increased incidence of PDs (incidence rate ratio [IRR] 1.72; 95%CI: 1.47-2.01) as compared to the matched cohort, which remained consistent over time. The incidence of PDs was similarly elevated in IBD (IRR 2.19; 95%CI: 1.69-2.84), MS (IRR 1.79; 95%CI: 1.29-2.50) and RA (IRR 1.61; 95%CI: 1.29-1.99). Lower socioeconomic status and urban residence were associated with an increased incidence of PDs, whereas mid to older adulthood (age 45-64) was associated with overall decreased incidence. In a restricted sample with 5 years of data before and after IMID diagnosis, the incidence of PDs was also elevated before IMID diagnosis among all IMID groups relative to matched controls. CONCLUSIONS: IMID are associated with an increased incidence of PDs both before and after an IMID diagnosis. These results support the relevance of shared risk factors in the co-occurrence of PDs and IMID conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".