Behavioural interventions in the rehabilitation of acute <i>v.</i> chronic non-organic (conversion/factitious) motor disorders
Bibliographic record
Abstract
BACKGROUND: Repeated case series have documented the effectiveness of multidisciplinary in-patient behavioural treatment for conversion disorders. However, in the absence of controlled research, treatment success could be attributed to providing patients with a face-saving opportunity to get better. AIMS: The present study contrasts two behavioural treatments to elucidate the factors underlying successful in-patient rehabilitation of this population. METHOD: Thirty-nine patients underwent a standard behavioural programme. Using a crossover design, patients who did not improve underwent a strategic-behavioural treatment in which they and their families were told that full recovery constituted proof of an organic aetiology whereas failure to recover was definitive proof of a psychiatric aetiology. RESULTS: Chart review indicated that the standard behavioural treatment was effective for 8/9 'acute' patients but only for 1/28 'chronic' patients. Of the 21 patients with chronic motor disorder who then under went the strategic-behavioural intervention, 13 were symptom-free at discharge. CONCLUSIONS: The strategic intervention was superior to standard behavioural treatment for patients with chronic motor disorder. Treatment components previously deemed critical for the effectiveness of behavioural treatment may be unnecessary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".