The Dark Side of the Moon: Meta-analytical Impact of Recruitment Strategies on Risk Enrichment in the Clinical High Risk State for Psychosis
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Bibliographic record
Abstract
BACKGROUND: The individual risk of developing psychosis after being tested for clinical high-risk (CHR) criteria (posttest risk of psychosis) depends on the underlying risk of the disease of the population from which the person is selected (pretest risk of psychosis), and thus on recruitment strategies. Yet, the impact of recruitment strategies on pretest risk of psychosis is unknown. METHODS: Meta-analysis of the pretest risk of psychosis in help-seeking patients selected to undergo CHR assessment: total transitions to psychosis over the pool of patients assessed for potential risk and deemed at risk (CHR+) or not at risk (CHR-). Recruitment strategies (number of outreach activities per study, main target of outreach campaign, and proportion of self-referrals) were the moderators examined in meta-regressions. RESULTS: 11 independent studies met the inclusion criteria, for a total of 2519 (CHR+: n = 1359; CHR-: n = 1160) help-seeking patients undergoing CHR assessment (mean follow-up: 38 months). The overall meta-analytical pretest risk for psychosis in help-seeking patients was 15%, with high heterogeneity (95% CI: 9%-24%, I (2) = 96, P < .001). Recruitment strategies were heterogeneous and opportunistic. Heterogeneity was largely explained by intensive (n = 11, β = -.166, Q = 9.441, P = .002) outreach campaigns primarily targeting the general public (n = 11, β = -1.15, Q = 21.35, P < .001) along with higher proportions of self-referrals (n = 10, β = -.029, Q = 4.262, P = .039), which diluted pretest risk for psychosis in patients undergoing CHR assessment. CONCLUSIONS: There is meta-analytical evidence for overall risk enrichment (pretest risk for psychosis at 38 monhts = 15%) in help-seeking samples selected for CHR assessment as compared to the general population (pretest risk of psychosis at 38 monhts=0.1%). Intensive outreach campaigns predominantly targeting the general population and a higher proportion of self-referrals diluted the pretest risk for psychosis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it