Evaluation of an Inference‐Based Approach to Treating Obsessive‐Compulsive Disorder
Bibliographic record
Abstract
This study evaluated an inference-based approach (IBA) to the treatment of obsessive-compulsive disorder (OCD) by comparing its efficacy with a treatment based on the cognitive appraisal model (CAM) and exposure and response prevention (ERP). IBA considers initial intrusions in OCD (e.g. "Maybe the door is open", "My hands could be dirty") as idiosyncratic inferences about possible states of affairs arrived at through inductive reasoning. In IBA such primary inferences represent the starting point of obsessional doubt, and the reasoning maintaining the doubt forms the focus for therapy. This is unlike CAM, which regards appraisals of intrusions as the maintaining factors in OCD. Fifty-four OCD participants, of whom 44 completed, were randomly allocated to CAM, ERP or IBA. After 20 weeks of treatment all groups showed a significant reduction in scores on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and the Padua Inventory. Participants with high levels of obsessional conviction showed greater benefit from IBA than CAM. Appraisals of intrusions changed in all treatment conditions. Strength of primary inference was not correlated with symptom measures except in the case of strong obsessional conviction. Strength of primary inference correlated significantly with the Y-BOCS insight item. Treatment matching for high and low conviction levels to IBA and CAM, respectively, may optimize therapy outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".