Impact of gaming disorder on first episode psychosis patients' evolution: Protocol for a multicentered prospective study
Bibliographic record
Abstract
AIMS: The objective of this study is to underline the impact of Gaming Disorder on the clinical evolution of patients with First Episode Psychosis. The specific aims of the study are to determine the prevalence of gaming disorder among those patients and assess the consequences of gaming on their clinical trajectory. METHODS: This is a prospective multicenter cohort study that will enrol 800 patients diagnosed with a first episode psychosis, with a follow-up period of up to 3 years. Using a systematic screening procedure for gaming disorder, the clinical staff will assess patients gaming habits at admission and every 6 months thereafter. Information from patients' medical records will also be extracted using the same timeframe. RESULTS: The patients' characteristics at admission and during follow-up will be presented in the form of descriptive statistics and compared between different subgroups of patients using uni- and multivariate logistic regression models. Repeated measures ANCOVA will also be performed to analyse the impact of gaming disorders on patients' clinical path as assessed by the Positive and Negative Syndrome Scale and the Clinical Global Impression scale, considering covariates such as psychiatric diagnosis, pharmacological treatment, age, sex/gender, and duration of untreated psychosis. CONCLUSION: These findings will guide the development of prevention, detection, and treatment strategies for the comorbidity between gaming disorder and first episode psychosis, ultimately improving the patients' recovery.
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".