Panic Disorder and Agoraphobia: A Brief Overview and Guide to Assessment
Bibliographic record
Abstract
A thorough assessment is essential for defining the targets for therapy, developing an individually tailored treatment plan, setting treatment goals, and measuring treatment progress and outcome. It is important to consider medical conditions and substances that may mimic panic symptoms. Determining whether panic attacks are uncued, assessing the focus of fear during a panic attack, and examining reasons for avoidance of situations are crucial to distinguishing PD from other anxiety disorders. Assessment of the nature and frequency of panic attacks and the core features of PD (interoceptive anxiety, panic cognitions, and agoraphobic avoidance) forms the basis for choosing treatment strategies, and periodic monitoring of these components provides indicators of treatment progress and outcome. Finally, comorbidity, levels of impairment, course, and family variables are also important factors to consider when developing and implementing treatment. Ideally, assessment should be multimodal, utilizing a combination of clinical interviews, self-report measures, self-monitoring diaries, and BATs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".