Effectiveness of Saikokaryukotsuboreito (Herbal Medicine) for Antipsychotic-Induced Sexual Dysfunction in Male Patients with Schizophrenia: A Description of Two Cases
Bibliographic record
Abstract
Antipsychotics sometimes cause sexual dysfunction in people with schizophrenia. The authors report the effectiveness of Saikokaryukotsuboreito (Japanese traditional herbal medicine, Chai-Hu-Jia-Long-Gu-Mu-Li-Tang in Chinese) for antipsychotic-induced sexual dysfunction in two male patients with schizophrenia. The first patient was a 28-year-old man with schizophrenia who suffered erectile dysfunction induced by olanzapine 10 mg/day; the erectile dysfunction significantly improved following the treatment of Saikokaryukotsuboreito 7.5 g/day. The other case was a 43-year-old man with schizophrenia who was receiving fluphenazine decanoate at 50 mg/month and had difficulties in ejaculation; add-on of Saikokaryukotsuboreito 7.5 g/day recovered his ejaculatory function. There has been no report on the effectiveness of Japanese herbal medicine formulations for antipsychotic-induced sexual dysfunction. Although the effectiveness of Saikokaryukotsuboreito needs to be tested in systematic clinical trials, this herbal medicine may be a treatment option to consider for this annoying side effect.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".