The differential calibration of the HPA axis as a function of trauma versus adversity: A systematic review and p-curve meta-analyses
Bibliographic record
Abstract
Although there is an abundance of evidence linking the function of the hypothalamic-pituitary-adrenal (HPA) axis to adverse early-life experiences, the precise nature of the association remains unclear. Some evidence suggests early-life adversity leads to cortisol hyper-reactivity, while other evidence suggests adversity leads to cortisol hypo-reactivity. Here, we distinguish between trauma and adversity, and use p-curves to interrogate the conflicting literature. In Study 1, trauma was operationalized according to DSM-5 criteria; the p-curve analysis included 68 articles and revealed that the literature reporting associations between trauma and blunted cortisol reactivity contains evidential value. Study 2 examined the relationship between adversity and cortisol reactivity. Thirty articles were included in the analysis, and p-curve demonstrated that adversity is related to heightened cortisol reactivity. These results support an inverted U-shaped function relating severity of adversity and cortisol reactivity, and underscore the importance of distinguishing between “trauma” and “adversity”.
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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".