Optimizing Treatment in Social Phobia: A Review of Treatment Resistance
Bibliographic record
Abstract
Over the last 25 years, there has been a rapid expansion of our knowledge base of social phobia (SP). Although there are a number of well-validated treatment modalities, including pharmacotherapy and cognitive-behavioral therapy, significant gaps remain in our ability to achieve full remission in most patients. Despite advances in the neurobiology of SP, the etiology has yet to be determined. Investigations examining potential predictors of response have provided little guidance in selecting an appropriate treatment modality. These gaps in our knowledge have pushed us to examine issues related to treatment resistance. This paper presents a review of the current literature and issues related to treatment-resistant SP, including a discussion of the functional impairment associated with SP, definitions of treatment response and remission, as well as outcome measures that have been used in clinical investigations of SP. In addition, criteria for a standard treatment trial, predictors of treatment response, a review of treatment resistance studies, and potential directions for future research are examined. The most promising strategies to attain remission, will likely involve augmenting selective serotonin reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors with agents such as anticonvulsants, benzodiazepines, and antipsychotics as well as combining pharmacotherapy with cognitive-behavioral therapy. Our current treatment target of simply attaining a response needs to be refocused, so that an asymptomatic state and high end state functioning become the final goal of treatment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".