From the laboratory to the clinic (and back again): How experiments have informed cognitive–behavior therapy for obsessive–compulsive disorder
Bibliographic record
Abstract
Behavioral and cognitive models—as well as complementary theories such as the inference-based, mood-as-input, and seeking proxies for internal states approaches—have been put forward to explain the development and maintenance of obsessive–compulsive disorder (OCD). Although theory is important to inform the conceptualization and treatment of OCD, experimental research is essential to provide empirical support for these different theoretical approaches. Experiments allow an increased understanding of the mechanisms (e.g., maladaptive beliefs) associated with the etiology and maintenance of OCD symptoms and, in this way, directly contribute to the expansion and creation of cognitive–behavioral treatment strategies. This selective review demonstrates how foundational and sometimes groundbreaking experiments pertaining to core OCD symptoms (i.e., checking/reassurance seeking, obsessions, contamination, and ordering/arranging) have informed the improvement of cognitive–behavior therapy for this debilitating mental illness. The relevance of experiments with both clinical and analog samples is discussed, and recommendations for future experimental work are provided.
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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 | 0.024 | 0.044 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".