Berkson’s bias in biobank sampling in a specialised mental health care setting: a comparative cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To determine whether studying aetiological pathways of depression, in particular the well-established determinant of childhood trauma, only in a specialised mental healthcare setting can yield biased estimates of the aetiological association, given that the majority of individuals are treated in primary care settings. DESIGN AND SETTING: Two databanks were used in this study. The Canadian Community Health Survey (CCHS) on Mental Health and Well-Being 2012 is a national survey about mental health of adult Canadians. It measured common mental disorders and utilisation of services. The Signature mental health biobank includes adults from the Island of Montreal recruited at the emergency department of a major university mental health centre. After consent, participants filled standardised psychosocial questionnaires, gave blood samples, and their clinical diagnosis was recorded. We compared the cohort of depressed individuals from CCHS and Signature in contact with specialised services with those in contact with primary care or not in treatment. PARTICIPANTS: There were 860 participants with depression in the CCHS and 207 participants with depression in the Signature Bank. PRIMARY AND SECONDARY OUTCOMES: The Childhood Experiences of Violence Questionnaire was used to measure childhood trauma in both settings. Childhood trauma is associated with depression as with other common mental and physical disorders. RESULTS: Individuals with depression in the CCHS who reported having been hospitalised for psychiatric treatment or having seen a psychiatrist or those from Signature were found to be more strongly associated with childhood abuse than individuals with depression who were treated in primary care settings or did not seek mental healthcare in the preceding year. CONCLUSIONS: Berkson's bias limits the generalisability of aetiological associations observed in such university-hospital-based biobanks, but the problem can be remedied by broadening recruitment to primary care settings and the general population.
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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 | 0.114 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".