Uncovering the heterogeneity of posttraumatic stress disorder: Towards a personalized medicine approach for military members and Veterans
Bibliographic record
Abstract
Introduction: Recently, there has been substantial interest in exploring the heterogeneity of posttraumatic stress disorder (PTSD) on a neurobiological level, as individuals with PTSD, including military members and Veterans, vary in their presentation of symptoms. Methods: Critically, a dissociative subtype of PTSD (PTSD+DS) has been defined, where a large body of evidence suggests that the unique presentation of symptoms among PTSD+DS patients is associated with aberrant neurobiological underpinnings. Results: PTSD+DS is often characterized by emotion overmodulation, with increased top-down activation from emotion regulation areas, which is associated with emotional detachment, depersonalization, and derealization. This is in stark contrast to the symptoms commonly observed in individuals with PTSD, who exhibit emotion undermodulation, which involves decreased top-down regulation of hyperactive emotion generation areas and is associated with vivid re-experiencing of trauma memories and hyperarousal. Discussion: This article examines a clinical case example that clearly illustrates this heterogeneous presentation of PTSD symptomatology and psychopathology. It discusses the implications this evidence base holds for a neurobiologically-informed, personalized medicine approach to treatment for military members and Veterans.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".