Catecholamines in Post-traumatic Stress Disorder: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Studies on the association between post-traumatic stress disorder (PTSD) and levels of catecholamines have yielded inconsistent results. The aim of this study was to conduct a systematic review and meta-analysis to assess whether concentrations of the catecholamines dopamine, norepinephrine, and epinephrine are associated with PTSD. This study searched relevant articles in the following databases: PubMed, Embase, Web of Science, and Psyc-ARTICLES. Each database was searched from its inception to September, 2018. Data related to catecholamine concentrations were extracted for patients with PTSD and the controls to calculate standardized mean differences and to evaluate effect sizes. A meta-analysis was then performed to compare the concentration of each catecholamine between the two groups in blood and/or urine samples. Heterogeneity was quantified using I2 and its significance was tested using the Q statistics. Subgroup analyses of the types of controls, PTSD assessment tools, and assayed methods used in the studies were performed to explore sources of heterogeneity among studies. Random-effects models were used to combine results from selected studies. A total of 1,388 articles were identified, of which 27 were included in the final analysis. Heterogeneity was high; hence random-effects models were used to combine results of selected studies. Results revealed significantly higher norepinephrine levels in people with PTSD than in the controls (standardized mean difference (SMD) =0.35, 95% confidence interval (CI): 0.13 to 0.57, p=0.002). No difference was found in dopamine and epinephrine concentrations between the two groups. Elevated norepinephrine levels may be an important indicator for PTSD.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.003 | 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".