Eye-tracking in adult depression: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: In recent years, eye-tracking has been proposed as a promising tool to identify potential biomarkers for mental disorders, including major depression. We will conduct an updated systematic review and meta-analysis on eye-tracking research in adults with major depressive disorder or other clinically diagnosed depressive disorders. METHODS AND ANALYSIS: This protocol follows all reporting items in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol extension. We will conduct a systematic search of PubMed, PsycINFO, Google Scholar and EMBASE for sources published up until March 2023. Abstract and full-text review will be completed independently by two reviewers. Non-randomised studies using eye movement tasks in individuals with a depressive disorder versus controls will be included. Eye movement tasks of interest include, but are not limited to, saccade, smooth pursuit, fixation, free-viewing, attentional disengagement, visual search and attentional blink tasks. Results will be categorised by eye movement task. Risk of bias will be assessed using the National Institutes of Health Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies and confidence in cumulative evidence will be assessed using Grading of Recommendations, Assessment, Development and Evaluation criteria. ETHICS AND DISSEMINATION: Ethics approval is not required due to the nature of the proposed analysis. Results will be disseminated through a journal article, conference presentations and/or dissertations.
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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 | 0.074 | 0.122 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.025 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.011 |
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".