Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To aid in the differentiation of individuals with major depressive disorder (MDD) from healthy controls, numerous peripheral biomarkers have been proposed. To date, no comprehensive evaluation of the existence of bias favoring the publication of significant results or inflating effect sizes has been conducted. METHODS: Here, we performed a comprehensive review of meta-analyses of peripheral nongenetic biomarkers that could discriminate individuals with MDD from nondepressed controls. PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched through April 10, 2015. RESULTS: From 15 references, we obtained 31 eligible meta-analyses evaluating biomarkers in MDD (21,201 cases and 78,363 controls). Twenty meta-analyses reported statistically significant effect size estimates. Heterogeneity was high (I2 ≥ 50%) in 29 meta-analyses. We plausibly assumed that the true effect size for a meta-analysis would equal the one of its largest study. A significant summary effect size estimate was observed for 20 biomarkers. We observed an excess of statistically significant studies in 21 meta-analyses. The summary effect size of the meta-analysis was higher than the effect of its largest study in 25 meta-analyses, while 11 meta-analyses had evidence of small-study effects. CONCLUSIONS: Our findings suggest that there is an excess of studies with statistically significant results in the literature of peripheral biomarkers for MDD. The selective publication of 'positive studies' and the selective reporting of outcomes are possible mechanisms. Effect size estimates of meta-analyses may be inflated in this literature.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it