Towards a major methodological shift in depression research by assessing continuous scores of recurrence of illness, lifetime and current suicidal behaviors and phenome features
Bibliographic record
Abstract
BACKGROUND: The binary major depressive disorder (MDD) diagnosis is inadequate and should never be used in research. AIMS: The study's objective is to explicate our novel precision nomothetic strategy for constructing depression models based on adverse childhood experiences (ACEs), lifetime and current phenome, and biomarker (atherogenicity indices) scores. METHODS: This study assessed recurrence of illness (ROI: namely recurrence of depressive episodes and suicidal behaviors, SBs), lifetime and current SBs and the phenome of depression, neuroticism, dysthymia, anxiety disorders, and lipid biomarkers including apolipoprotein (Apo)A, ApoB, free cholesterol and cholesteryl esters, triglycerides, high density lipoprotein cholesterol in 67 normal controls and 66 MDD patients. We computed atherogenic and reverse cholesterol transport indices. RESULTS: We were able to extract one factor from a) the lifetime phenome of depression comprising ROI, and traits such as neuroticism, dysthymia and anxiety disorders, and b) the phenome of the acute phase (based on depression, anxiety and quality of life scores). PLS analysis showed that 55.7 % of the variance in the lifetime + current phenome factor was explained by increased atherogenicity, neglect and sexual abuse, while atherogenicity partially mediated the effects of neglect. Cluster analysis generated a cluster of patients with major dysmood disorder, which was externally validated by increased atherogenicity and characterized by increased scores of all clinical features. CONCLUSIONS: The outcome of depression should not be represented as a binary variable (MDD or not), but rather as multiple dimensional scores based on biomarkers, ROI, subclinical depression traits, and lifetime and current phenome scores including SBs.
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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 | 0.229 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".