Maladaptive Eating in Posttraumatic Stress Disorder: A Population‐Based Examination of Typologies and Medical Condition Correlates
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) and eating pathology are frequently comorbid, and both are independent risk factors for various medical conditions. Using population-based data collected as part of the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III; N = 36,309), the primary objectives of this study were to (a) identify eating pathology classes among PTSD and (b) investigate associations between maladaptive eating and medical conditions among PTSD. Using the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-5), we assessed PTSD and maladaptive eating symptoms in accordance with the DSM-5. We used a latent class analysis to identify maladaptive eating typologies among adults with lifetime PTSD (n = 2,339; 6.1%) and multivariable logistic regression models to examine associations between each of the six emergent maladaptive eating typologies and medical conditions. Results revealed that over 40% of individuals with PTSD endorsed indicators of maladaptive eating. In addition, each maladaptive eating typology among PTSD was significantly associated with unique sociodemographic characteristics and increased odds of medical conditions relative to no PTSD and no eating disorder, adjusted odds ratios (AORs) = 1.34-6.55, and PTSD with no eating psychopathology, AORs = 1.43-5.11. Results of this study provide a better understanding of maladaptive eating in adults with PTSD and potential medical sequelae. Results indicate maladaptive eating may be an important mechanism in the association between PTSD and medical conditions, which may inform targeted interventions among individuals with these comorbidities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".