Reboxetine Adjuvant Therapy in Patients With Schizophrenia Showing a Suboptimal Response to Clozapine
Bibliographic record
Abstract
The present 12-week open-label uncontrolled trial was aimed to explore the efficacy of reboxetine add-on pharmacotherapy on clinical symptoms and cognitive functioning in 15 patients with schizophrenia with suboptimal response (mean [SD] Brief Psychiatric Rating Scale baseline total score, 32.2 [5.4]) despite receiving clozapine monotherapy at the highest tolerated dosage. The results obtained evidenced that reboxetine at a dosage of 4 mg/d mildly reduced only depressive symptoms (Calgary Depression Scale for Schizophrenia: P = 0.035, Cohen d = 0.7), whereas worsening of performances on phonemic fluency (P = 0.012, Cohen d = 0.5) was observed. After Bonferroni correction, changes at the Calgary Depression Scale for Schizophrenia and at the Verbal Fluency Task were not further confirmed.The results obtained indicate that reboxetine seemed to be scarcely effective for reducing clinical symptoms in patients with schizophrenia who have had an incomplete clinical response to clozapine. Regarding cognitive functioning, in our sample, a trend to experience cognitive impairment in the examined domains was observed, as confirmed by a mild worsening of performances on cognitive tasks.Schizophrenia is a heterogeneous disorder with regard to pathophysiology; therefore, data reflecting the mean response of a sample of patients may fail to reveal therapeutic effects. More research is needed to better identify subgroups of patients with peculiar features, which may account for responsivity to experimental medications and augmentation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".