�븳援��삎 怨듯솴�옣�븷 �빟臾쇱튂猷� �븣怨좊━�벉, 2008 : 珥덇린移섎즺�쟾�왂
Bibliographic record
Abstract
The Korean Association of Anxiety Disorders developed a Korean treatment algorithm for panic disorder to help clinicians make treatment decisions. This study investigated a consensus about initial treatment strategies as part of developing a medication algorithm for panic disorders in Korea. Methods竊숥ased on current treatment algorithms published by the American Psychiatric Association, the National Institute for Clinical Excellence,\n\nand the Canadian Psychiatric Association, we developed questionnaires about initial treatment strategies for patients with panic disorder. Fifty-four experts in panic disorder answered the questionnaires. We classified\n\nexpert opinions into three categories (first-, second-, and third-line treatment strategies) by ��2 tests. Results竊숤ntidepressants and anxiolytics were recommended as first-line strategies for the initial treatment of panic disorder. A combination of medical treatment and cognitive-behavioral therapy was also recommended for more severe cases. Paroxetine, escitalopram, alprazolam, and clonazepam were preferred from among many anti-panic drugs. The mean starting dose of anti-panic drugs in the initial treatment for panic disorder was relatively lower than that for such other psychiatric illnesses as major depressive disorder. Conclusion竊숿hese results, reflecting recent studies and clinical experiences, may provide guidelines about initial treatment strategies for panic disorder.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".