The Sensitivity of Three Versions of the Padua Inventory to Measuring Treatment Outcome and Their Relationship to the Yale-Brown Obsessive Compulsive Scale
Bibliographic record
Abstract
The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and different versions of the Padua Inventory (PI) are frequently used instruments to measure symptoms of obsessive-compulsive disorder (OCD). However, little is known of how these different versions of the PI compare to each other in their sensitivity to measuring treatment outcome, and there is currently no adequate explanation to account for the weak relationships between self-report measures and the Y-BOCS. This study aimed to investigate the sensitivity of these measures to treatment outcome, and to examine whether differences in how they measure symptom severity can explain the weak relationships. Hypotheses were: (1) the Y-BOCS would be significantly more sensitive to measuring treatment outcome than the PI versions; (2) correlations between the measures would be significantly stronger for change scores as compared to relations measured at a single point in time; (3) weak relationships can be explained by the PI measuring symptom severity based on content and the Y-BOCS measuring symptoms, independent of content. Results showed that the Y-BOCS was significantly more sensitive to measuring treatment outcome than the PI versions, while differences between the questionnaires in which severity is measured can provide a partial account for why weak relations are observed between these measures.
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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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".