Evidence map of nutrient interventions for major depression: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Depression severely impacts quality of life globally. While traditional treatments show efficacy, many patients respond poorly. Nutritional interventions demonstrate potential as safe, economical adjunctive therapies. However, current evidence is scattered and heterogeneous, lacking systematic evaluation. This study aims to systematically evaluate evidence for various nutritional interventions in depression and create an evidence gap map (EGM). METHODS: We will conduct a systematic review using the EGM approach. Major medical databases will be searched for relevant studies published up to present. Two independent researchers will screen literature, extract data, and assess evidence quality. Multiple tools will be used for quality assessment: Cochrane Risk of Bias Tool 2.0 for RCTs, ROBINS-I for non-RCTs, Newcastle-Ottawa Scale for observational studies. The GRADE system will be employed for overall evidence grading. EPPI-Reviewer Web will generate the EGM. We will analyze evidence distribution, time trends, and explore impacts of population characteristics and depression subtypes. DISCUSSION: This study will provide critical methodological references for nutritional psychiatry. Expected results include identifying nutrient categories with robust antidepressant evidence, evaluating evidence distribution for different interventions, and exploring differential effects across populations and depression subtypes. Findings will inform individualized nutritional strategies, improve clinical guidelines, and potentially influence public health policies. SYSTEMATIC REVIEW REGISTRATION: CRD42024590644. Date of registration: 30/09/2024.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.118 | 0.014 |
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".