Case-finding for depression in primary care (CAIRO): a multicentre, cross-sectional study in England
Bibliographic record
Abstract
OBJECTIVES: To examine the number of patients screening positive for depression, while self-completing an automated check-in screen prior to a general practice consultation. DESIGN: A descriptive cross-sectional study. SETTING: 10 general practices in the West Midlands, England. Recruitment commenced in March 2023 and concluded in June 2023. PARTICIPANTS: All patients aged 18 years and over, self-completing an automated check-in screen for any general practice prebooked appointment, were invited to participate during a 3-week recruitment period. PRIMARY AND SECONDARY OUTCOME MEASURES: The number of patients screening positive for depression using the Whooley case finding research questions was the primary outcome measure. Secondary outcome measures included: demographic and (general practice level) deprivation differences in completion responses. RESULTS: 73.5% (n=3666) of patients self-completing an automated check-in screen participated in the CAse-fInding foR depressiOn in primary care (CAIRO) study, (61.1% (n=2239) female, mean age 55.0 years (18-96 years, SD=18.5)).28.3% (n=1039) of participants provided a positive response to at least one of the two Whooley research questions (31.2% female and 23.8% male). Significantly more positive responses were obtained from females, those aged between 35 years and 49 years and those from more deprived practices. CONCLUSIONS: Over a quarter of CAIRO participants provided a positive response to at least one of the two Whooley questions, suggesting possible unmet need in the population studied. A follow-up study could investigate whether responses provided at the point of check-in are raised and addressed in the subsequent consultation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".