Switching atypical antipsychotics: a review
Bibliographic record
Abstract
BACKGROUND: Atypical antipsychotics are increasingly used in the treatment of diverse psychiatric disorders; however, there is little information on the 'why, when, and how' of switching between the different atypical antipsychotics currently available. OBJECTIVE: To review the data on switching and atypical antipsychotics. METHODS: A literature search was initially conducted using the key words followed by a search of relevant articles including conference abstracts; relevant pharmaceutical companies were also contacted. RESULTS: Clinical trial data are limited in terms of parameters measured, and case reports describe specific problems. Few studies are based on real world populations of psychiatric patients over the long-term. Careful patient and drug selections matched to a carefully supervised and appropriate cross titration based upon the pharmacodynamic and pharmacokinetic properties of all of the drugs involved is important to avoid potential complications such as re-emergence or worsening of psychosis and withdrawal, rebound, and emergent phenomena including new or uncovered side-effects. Psychoeducation and involvement of patients and caregivers in the process are also necessary for a successful switch. CONCLUSION: Despite the prevalence of switching in real world clinical practice, there is a paucity of data to guide clinicians with respect to effective and safe strategies. There are no criteria defining a successful switch. With the increasing range and formulations of atypical antipsychotics available, there is a rationale for their early use to avoid the practical problems associated with switching from conventional antipsychotics as well as the opportunity to maintain patients on an optimal atypical antipsychotic monotherapy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".