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Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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 it