Re: Community Treatment Orders for Psychiatric Patients: The Emperor with No Clothes
Bibliographic record
Abstract
Dear Editor: We appreciated reading the spirited debate between Dr O'Reilly, on the one hand, and Dr Kisely and Ms Campbell, on the other, about community treatment orders (CTOs)14 and were pleased to see that the results of our North Carolina study5 continue to animate discussion of this important and controversial topic. Clearly, the use of legal leverage in community-based mental health treatment poses challenging questions for research and policy; it is hardly surprising that experts disagree, in good faith, about both the intervention and the evidence for its effectiveness. Several points underlying Kisely and Campbell's critique of our study in North Carolina warrant further comment. First, Kisely and Campbell write, The 1-year follow-up is therefore of a highly selected and potentially unrepresentative population that was not dangerous and was sufficiently compliant to participate in baseline and follow-up assessments.1,p 684 The attrition problem that these authors highlight (18% in our study) was substantial but irrelevant to the hospital outcome data. We included admissions data for all study participants-even dropouts-in the intent-to-treat analysis of hospital recidivism. Kisely and Campbell's suggestion that our study excluded all patients who were dangerous requires a qualifier. The exclusion criterion applied to patients with a documented recent history of serious violent behaviour involving weapon use or causing physical injury. However, one-third of randomized participants had engaged in acts of simple battery, or had been involved in physical fights, during the 4 months preceding enrolment. An additional 20% had made verbal threats of harm to others. Suicidality was also not an exclusion criterion. We think our study generalizes to a broader clinical population than one might imagine from reading Kisely and Campbell's critique. Second, these authors write that the study failed to show significant differences between intervention and control groups in terms of hospital or other outcomes ... over the following 12 months.1 In fact, using repeated-measures analysis, we found that assignment to the outpatient commitment group was associated with a significantly lower odds of any readmission (odds ratio 0.64; 95% confidence interval, 0.46 to 0.88; P As Kisely and Campbell correctly observe, our analysis also revealed that the apparent effect of the intervention was concentrated heavily among participants who received extended court-ordered treatment. Still, the result was sufficiently strong to achieve statistical significance for the experimental group as a whole. Finally, Kisely and Campbell state, Analysis of subjects who have not been randomly assigned to CTO groups of less or more than 180 days may reflect a bias where a CTO was selectively extended when it seemed to be helping the patient.1, p 684 Our evidence suggests the opposite was true. Participating mental health centres agreed in advance to systematically review each expiring court order and file a petition to renew if the patient continued to meet legal criteria for outpatient commitment. Consequently, patients with a history of treatment noncompliance and poor insight were significantly more likely to have their orders renewed. This possibly amounted to a selection bias, but one favouring a negative finding, making it harder-not easier-to show an effect for CTOs. In the end, some will be persuaded by these results and others will not. As researchers, we take no advocacy position with respect to the policy of CTOs. Our goal has been to help build a base of evidence to inform the policy debate. Clearly, however, this is an area that warrants careful reflection as well as further research in different populations and jurisdictions. …
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".