CP for obsessive-compulsive disorder and post-traumatic stress disorder
Bibliographic record
Abstract
Two OCD (DSM-III) patients with marked checking rituals who had not improved after much ftf guidance in exposure and ritual prevention improved when they were given OCCheck to use on a laptop computer at home and on a handheld (palmtop) computer carried outside the home in order to facilitate compliance (Table 3.1: Baer et al., 1987, 1988). They worsened when they did not use the computers and improved again on reinstating use of the computer. Two other patients refused to use OCCheck. This is an interactive-voice-response (IVR) system which OCD patients reach by phoning a toll-free number to a computer while looking at an accompanying workbook. The 800 or so voice ®les of BTSteps (BTS ) guide users to set and monitor individually tailored self-exposure and ritual prevention (ERP) homework, keep a diary, make baseline and later ratings, get feedback on progress, deal with dif®culties, and prevent relapse. BTS assumes an understanding/reading age of 11. BTS has been the subject of three open studies and two RCTs. Several studies were completed of BTS&s;s phone-IVR + workbook version in OCD patients in the UK, USA and Canada in varying proportions across different trials. An even more convenient internet version (OCFighter) using BTS&s;s phone-IVR algorithms will soon allow home access with no need for printed booklets and phone-IVR calls.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".