Turning order into chaos through repetition and addition of elementary acts in obsessive-compulsive disorder (OCD)
Bibliographic record
Abstract
A concept and methodology derived from an animal model provided the framework for a study of rituals in obsessive compulsive disorder (OCD) patients and yielded objective and observable criteria applicable for compulsive rituals across patients. The employed ethological approach should be able to reveal and identify a common structure underlying OCD rituals, pointing to shared psychopathology. Eleven OCD rituals performed by patients in their own home were videotaped and compared with the behaviour of healthy individuals instructed to perform the same rituals. The videotaped rituals were deconstructed into visits to specific locations or objects (ritual space), and to the acts performed at each location/object (ritual basic components). Quantitative analyses revealed that compulsiveness emanates from the expansion of repeats for some acts and visits, and from the addition of superfluous act types. Best discrimination between OCD and control rituals (90.9% success) was provided by the parameter "maximum of act repeats in a ritual" (R(2)=0.77). It is suggested that the identified properties of compulsive behaviour are consistent with a recent hypothesis that ritualized behaviour shifts the individual's attention from a normal focus on structured actions to a pathological attraction onto the processing of basic acts, a shift that invariably overtaxes memory. Characteristics and mechanisms of compulsive rituals may prove useful in objective assessment of psychiatric disorders, behavioural therapy, and OCD nosology.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".