Biomarkers of stress resilience: A review
Bibliographic record
Abstract
The complex dyadic interaction of stress and resilience has received growing attention as a promising avenue for informing new diagnostic and prognostic models for human health. In this review, we present a selection of some of the most relevant data on translational models and biomarkers of stress and resilience in the field of mental health. Several critical aspects concerning the preclinical and clinical model development are addressed. The distance between preclinical and clinical disease models has widened with time across all fields of medicine, with psychiatry presenting additional hurdles represented by the inherent heterogeneity of the studied phenotypes. Capitalizing on technological advances in developing and consolidating sound theories for stress-resilience interaction models represents a promising avenue, possibly endowed with greater ecological validity compared to the sole socio-psychological assessment. Instrumental in advancing the field will be an increased level of integration between preclinical and clinical researchers' efforts in developing translational biomarkers, aiming to elucidate better the interindividual heterogeneity in the impact of stress exposure on individuals' health and behavior.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".