SERUM LIPID CONCENTRATIONS IN IRANIAN VETERANS WITH COMBAT-RELATED CHRONIC POSTTRAUMATIC STRESS DISORDER
Bibliographic record
Abstract
Objective: Several studies demonstrated changes in serum lipid concentrations in chronic post traumatic stress disorder (cPTSD) patients. The purpose of this study was to assess serum lipid concentrations in soldiers with cPTSD and compare it with chronic major depressive disorder (cMDD) patients. Methods: We measured serum lipid concentrations in patients with cPTSD (n=40) and cMDD (n=40) and compared the results. The patients were all male (30-48 years old) and had an illness history of more than 2 years prior to conducting the study. The groups were matched regarding their body mass index (BMI) and duration of symptoms. Laboratory investigations and psychiatric evaluations were carried-out 5 days after admission. Serum lipid concentrations were measured by enzyme assay (EA). Results: cPTSD group showed significantly greater mean cholesterol concentrations (227.3 ± 69.7 mg/dL) than the cMDD group (190.7 ± 35.4 mg/dL) (p=0.004). Mean high density lipoprotein cholesterol (HDL-C) concentrations for cPTSD patients (66.6 ± 17.6 mg/dl) was significantly lower than HDL-C level in cMDD patients (76.5 ± 19.7 mg/dL) (p = 0.02). In contrast, mean low density lipoprotein cholesterol (LDL-C) concentrations for cPTSD patients (118.9 ± 60.1 mg/dL) was significantly higher than LDL-C level in cMDD patients (76.5 ± 25.2 mg/dL) (p = 0.000). Although similar differences was noticed on triglycerides concentration (cPTSD; 220.2 ± 79.0 and cMDD; 201.0 ± 61.8), it was statistically non-significant (p = 0.23).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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; 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".