Eating Disorders in Clinical High-Risk for Psychosis Samples: a Study Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Many research and meta-analytic studies have shown that individuals at high clinical \nrisk for psychosis (CHR-P) often exhibit complex clinical conditions characterized by the co- \noccurrence of multiple psychopathological syndromes. Currently, evidence about the presence of \neating disorders (EDs) in this population is limited. Here we present a research protocol for a \nsystematic review and meta-analysis with the objectives of estimating the impact of \nsociodemographic and study variables on the ED diagnosis in CHR-P individuals and of resuming \nthe available knowledge on the features of EDs in CHR-P samples. \nMethods: PubMed, EBSCO/APA PsycINFO, and Web of Science will be searched for articles \npublished from 1 January 2018 to 31 December 2022. Original research assessing EDs in CHR-P \nsamples with validated measures will be included. Two independent researchers will screen the \narticles and evaluate their quality using a modified version of the Newcastle-Ottawa Scale–potential \ndisagreements will be solved by contacting a third judge. A narrative synthesis will be performed. \nMoreover, whenever applicable, random-effect models (proportions) and meta-regressions will be \napplied to extracted data. The study will be conducted using the PRISMA guidelines. \nDiscussion: Since the empirical literature regarding EDs in CHR-P samples is limited, this study \nintends to increase knowledge on the topic and inform preventive interventions aimed at addressing \nclinical challenges of CHR-P patients with ED.
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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 | 0.083 | 0.103 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".