Identification of possible neurobiological trait markers in high-risk subjects for panic disorder
Bibliographic record
Abstract
Panic disorder (PD) is a common illness that affects 3.5% of the general population. Several studies suggest that PD patients show psychophysiolgic or neuroendocrine alterations when compared to healthy control subjects. Studies measuring saccadic eye movement (SEM) suggest that PD patients have a decreased GABA-benzodiazepine receptor binding. Moreover, some studies suggest that PD is associated with hypothalamic-pituitary axis (HPA) disturbances. However, so far it is unclear whether these abnormalities represent state or trait specific alterations. To explore whether some of these measures could indicate an increased risk for PD, a study investigating 76 healthy offspring of patients with PD or healthy controls between 7 and 18 years was conducted. MANCOVA for peak saccadic eye velocity (pSEV), latency and accuracy, STAI-C state and trait scores with age and gender as covariates showed a significant overall group effect (F 5,68 =3.14, p=.013). High-risk subjects showed impaired accuracies (F 1,72 =6.52, p=.013) compared to low-risk controls. Moreover, the high risk group showed significant higher scores in the state (F 1,72 =4.36, p=.040) and trait (F 1,72 =9.62, p=.003) version of Spielbergers State-Trait Anxiety score. No effects were found for pSEV and latency. In summary, differences in some aspects of saccadic eye movement in subjects at high or low risk for panic were found. Implications for future research on possible predictors on PD will be discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".