Bibliographic record
Abstract
Back to table of contents Previous article Next article Clinical & Research NewsFull AccessSeveral Strategies Successful for Treating Anxiety DisordersJoan Arehart-TreichelJoan Arehart-TreichelSearch for more papers by this authorPublished Online:2 Oct 2009https://doi.org/10.1176/pn.44.19.0033bGeneralized anxiety disorder (GAD) can be seen in all clinical settings, and in primary care, practitioners are usually good at identifying GAD patients, Richard Swinson, M.D., said at the annual meeting of the Canadian Psychiatric Association in St. John's, Newfoundland, in August.“By the time [patients with GAD] see me, or other psychiatrists, they are longstanding worriers, that is, the more difficult cases,” said Swinson, a professor emeritus of psychiatry at McMaster University and medical director of the Anxiety Treatment Research Center at St. Joseph's Healthcare in Hamilton, Ontario.No matter the severity of GAD, there are effective treatments for it that can lead patients “to full recovery,” he said.Three SSRI antidepressants and two SNRI antidepressants have been approved for treating GAD in Canada, he noted, and there is some evidence that anticonvulsants or antipsychotics can help GAD patients. At first antipsychotics were used as SSRI augmenters for GAD, but a recent approach is to use them as monotherapy, and that has been found useful in clinical studies, he said.Cognitive-behavioral therapy (CBT) can also benefit GAD patients, Swinson noted. He said a good resource in this area is Michel Dugas, Ph.D., director of the anxiety disorders laboratory at Concordia University in Montreal. Over the past 16 years, Dugas has conducted research on the origins of GAD and has developed a CBT treatment for it, which has been validated in a number of trials. Dugas has also written a book called Cognitive-Behavioral Treatment for Generalized Anxiety Disorder: From Science to Practice, which provides a step-by-step treatment of GAD.For patients who have both GAD and depression, the best strategy is to place them on medications first and then to add CBT later, Swinson advised. The combination strategy should help alleviate both their anxiety and their depression, he indicated.Other common comorbid conditions for GAD include another anxiety disorder, alcohol abuse, personality disorders, and bipolar disorder, Swinson pointed out. ▪ ISSUES NewArchived
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.043 |
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".