Korean Guidelines For The Treatment Of Panic Disorder : Initial And Maintenance Treatment Strategies For The Pharmacological Treatment Of Panic Disorder
Bibliographic record
Abstract
Objective: Korean guidelines for treatment of panic disorder(PD) 2018 was developed. This study investigated the consensus among Korean experts regarding initial and maintenance pharmacological treatment strategies for the patients with PD in Korea.Method: The development committee for Korean guidelines for the treatment of panic disorder developed questionnaires pertinent to initial and maintenance treatment strategies for pharmacological treatment of PD, based on recent treatment guidelines published by the American Psychiatric Association, the National Institute for Clinical Excellence, and the Canadian Psychiatric Association. Seventy-two experts in PD answered questionnaires. We classified expert opinions into three categories, first, second, and third-line treatment strategies, by analyzing the 95% confidence interval. Results: Antidepressants, benzodiazepines and combined with cognitive-behavioral therapy(CBT) were recommended as treatments of choice(ToC), and first-line strategies for initial treatment of PD. Escitalopram, paroxetine, sertraline, and venlafaxine were preferred from among many anti-panic drugs. Mean starting dose of anti-panic drugs for initial treatment of PD was relatively lower, than that for other psychiatric illnesses such as major depressive disorder. In the case of maintenance treatment of PD, antidepressants and CBT were selected as ToC and first-line strategies. Patients were recommended typically to be examined every four weeks during treatment, to review effectiveness and side effects of the drug. The duration of maintenance pharmacological treatment was recommended to be continued for one year or more.Conclusion: These results, which reflect the recent studies and clinical experiences, may provide the guideline about optimal medication treatment strategies for PD.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".