Bibliographic record
Abstract
OBJECTIVE: Up to 40% of patients with schizophrenic psychoses have symptoms that are resistant to monotherapy with antipsychotic drugs. In consequence, combinations of drugs are often used, especially based on the antipsychotic agents clozapine and olanzapine because of their broad receptor-interaction profile. The aim of this review was to provide a critical overview of the published results of olanzapine augmentation. METHODS: A systematic database search was performed of MEDLINE and BIOSIS (Ovid), looking for publications on augmented therapeutic approaches involving olanzapine. The search terms used were "augmentation," "combination," "schizophrenia," "olanzapine," and the names of other antipsychotic drugs and non-antipsychotic agents, including brand names, spanning publications from 1966 until the end of December 2004. RESULTS: Of 14 reports dealing with 8 different antipsychotic augmentation strategies (83 patients), only 1 trial, of sulpiride-olanzapine therapy, was performed in a randomized manner. Based on clinical observation, a significant number of the treatments led to favourable results. In contrast to adjuvant therapy with antipsychotic drugs, augmentation of olanzapine with glycine, antidepressants or mood stabilizers was evaluated in well-designed clinical trials (8 publications, 989 patients), with distinct improvements of positive and/or negative symptoms reported. CONCLUSIONS: The combination of olanzapine with antidopaminergic atypical antipsychotic agents seems to follow a neurobiological rationale. The augmentation trials with non-antipsychotic agents, for example, mood stabilizers, were successful and showed that randomized and placebo-controlled trials are feasible. Therefore, systematic evaluations of antipsychotic agents as adjuvant therapy are possible as well as necessary to determine the benefits and risks of any new treatment strategy.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".