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Bibliographic record
Abstract
Objective : A working group of psychiatrists from the Korean Academy of Anxiety Disorders was established to determine the appropriate medication algorithm for treating patients with panic disorder. In this article, we discussed the consensus among psychiatrists regarding the use of cognitive behavior therapy (CBT) in the development of a treatment algorithm for panic disorder in Korea. Methods : Based on the guidelines or algorithms published by the American Psychiatric Association, National Institute for Clinical Excellence, and Canadian Psychiatric Association, we constructed questionnaires regarding the core components and contents of CBT for patients with panic disorder. Fifty-four experts in panic disorder completed the questionnaires. Results : There was statistically significant consensus among the experts in the belief that cognitive reconstruction and psychological education are the core components of CBT for the treatment of patients with panic disorder. However, there was some inconsistency between the opinions of some experts regarding the content and frequency of CBT and the results of studies published outside of Korea. Conclusions : CBT, especially the psychological education and cognitive reconstruction components, should be considered when treating patients with panic disorder. However, further consideration needs to be put into the design of a more detailed treatment guideline for the use of CBT in the treatment of patients with 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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".