Use of quetiapine in child and adolescent populations – Response to letter from Dr Lambe
Bibliographic record
Abstract
Australian & New Zealand Journal of Psychiatry, 47(11) Gentile S and Galbally M (2010) Prenatal exposure to antidepressant medications and neurodevelopmental outcomes: A systematic review. Journal of Affective Disorders 128: 1–9. Grigoriadis S, Vonderporten EH, Mamisashvili L, et al. (2013) Antidepressant exposure during pregnancy and congenital malformations: is there an association? a systematic review and meta-analysis of the best evidence. Journal of Clinical Psychiatry 74: e293–e308. Lewis A, Galbally M and Bailey C (2012) Perinatal mental health, antidepressants and neonatal outcomes: Findings from the Longitudinal Study of Australian Children. Neonatal, Paediatric and Child Health Nursing 15: 22–28. Malm H, Artama M, Gissler M, et al. (2011) Selective serotonin reuptake inhibitors and risk for major congenital anomalies. Obstetrics & Gynecology 118: 111–120. Myles N, Newall H, Ward H and Large M (2013) Systematic meta-analysis of individual selective serotonin reuptake inhibitor medications and congenital malformations. Australian and New Zealand Journal of Psychiatry 47: 1001–1011. O’Brien L, Einarson TR, Sarkar M, et al. (2008) Does paroxetine cause cardiac malformations? Journal of Obstetrics and Gynaecology Canada 30: 696–701. Rahimi R, Nikfar S and Abdollahi M (2006) Pregnancy outcomes following exposure to serotonin reuptake inhibitors: A meta-analysis of clinical trials. Reproductive Toxicology 22: 571–575. Wurst KE, Poole C, Ephross SA, et al. (2010) First trimester paroxetine use and the prevalence of congenital, specifically cardiac, defects: A meta-analysis of epidemiological studies. Birth Defects Research. Part A, Clinical and Molecular Teratology 88: 159–170.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".