Predictors of attrition during acute pharmacotherapy of psychotic depression in a clinical trial
Bibliographic record
Abstract
Little is known about factors that contribute to attrition in clinical trials of the pharmacotherapy of psychotic depression. The purpose of this study was to identify factors associated with attrition during acute pharmacotherapy in the Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II) clinical trial. Sociodemographic and clinical variables were assessed at baseline in 269 men and women, aged 18-85 years, who were treated with up to 12 weeks of open-label sertraline plus olanzapine. Univariate analyses examined the association of baseline variables with overall non-completion, as well as reasons for non-completion. Logistic regression was used to model the relationship of the significant univariate predictors with non-completion and its reasons. Seventy-four (27.5 %) participants did not complete the acute treatment phase of STOP-PD II. Male gender, younger age, inpatient status, higher Clinical Global Impression (CGI) severity of illness, and higher severity of psychomotor disturbance were associated with non-completion in univariate analyses. In regression models, higher CGI severity of illness score was the only significant independent predictor of non-completion, explained by withdrawal of consent. Our findings have implications for the retention of persons with psychotic depression in clinical trials.
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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 | 0.044 | 0.156 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".