Importance of genotyping and phenotyping of CYP450isoenzymes in the treatment of psychiatric disorders
Bibliographic record
Abstract
AMA Socha J, Kuczyńska J, Mierzejewski P. Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders. Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii. 2023;39(2):143-168. doi:10.5114/fpn.2023.129294. APA Socha, J., Kuczyńska, J., & Mierzejewski, P. (2023). Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders. Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii, 39(2), 143-168. https://doi.org/10.5114/fpn.2023.129294 Chicago Socha, Julita, Julita Kuczyńska, and Paweł Mierzejewski. 2023. "Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders". Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii 39 (2): 143-168. doi:10.5114/fpn.2023.129294. Harvard Socha, J., Kuczyńska, J., and Mierzejewski, P. (2023). Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders. Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii, 39(2), pp.143-168. https://doi.org/10.5114/fpn.2023.129294 MLA Socha, Julita et al. "Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders." Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii, vol. 39, no. 2, 2023, pp. 143-168. doi:10.5114/fpn.2023.129294. Vancouver Socha J, Kuczyńska J, Mierzejewski P. Importance of genotyping and phenotyping of CYP450 isoenzymes in the treatment of psychiatric disorders. Pharmacotherapy in Psychiatry and Neurology/Farmakoterapia w Psychiatrii i Neurologii. 2023;39(2):143-168. doi:10.5114/fpn.2023.129294.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".