Do sociodemographic and study variables impact eating disorder diagnosis in clinical high-risk for psychosis youth? A study protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Many studies have shown that youth at clinical high risk for psychosis (CHR-P) suffer from \nmultiple psychopathological syndromes. Currently, evidence about eating disorders (EDs) in this clinical \npopulation is limited. Here we present a research protocol for a systematic review and meta-analysis \naiming at estimating the impact of sociodemographic and study variables on the ED diagnosis in CHRP youth and of summarizing the available knowledge on clinical characteristics of CHR-P patients with \nEDs. Methods: PubMed, EBSCO/APA PsycINFO, and Web of Science will be searched for studies \npublished from 1 January 2018 to 31 December 2022. Original research evaluating EDs in CHR-P \nsamples with reliables instruments will be considered. Two independent reviewers will screen the studies \nand assess their quality with a modified version of the Newcastle-Ottawa Scale. Disagreements will be \nsolved by contacting a third judge. A narrative synthesis will be conducted. Whenever applicable, randomeffect models (proportions) and meta-regressions will be run to extracted data. This research will comply \nwith the PRISMA guidelines. Results: We expect to retrieve more cross-sectional than longitudinal studies. \nMeta-regression will examine the role of potential moderators. The narrative synthesis will focus on \nclinical characteristics of EDs in CHR-P youth, especially regarding functioning, symptoms, and bodily \nsensations. Discussion: Since the empirical literature regarding EDs in CHR-P samples is limited, this study \naims to add knowledge on the field and inform preventive interventions aimed at addressing EDs in \nCHR-P patients.
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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 | 0.142 | 0.173 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".