The addition of olanzapine to valproate or lithium for acute manic or mixed bipolar episodes reduced manic symptoms
Bibliographic record
Abstract
Tohen M, Chengappa KN, Suppes T, et al. Efficacy of olanzapine in combination with valproate or lithium in the treatment of mania in patients partially nonresponsive to valproate or lithium monotherapy. Arch Gen Psychiatry2002 Jan; 59 : 62 –9 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: In patients with acute manic or mixed bipolar episodes, is a combination of olanzapine with valproate or lithium more effective than valproate or lithium alone? 6 week randomised {allocation concealed*}†, blinded {patients, clinicians, data collectors, and outcome assessors}†,*, placebo controlled trial. 33 centres in the US and 5 in Canada. 344 patients (mean age 41 y, 52% women) who had bipolar disorder, manic (48%) or mixed (52%) episodes with or without psychotic episodes; had a score ≥16 (mean score 22) on the Young Mania Rating Scale (YMRS) at baseline; and had received treatment … [1]: {openurl}?query=rft.jtitle%253DArchives%2Bof%2BGeneral%2BPsychiatry%26rft.stitle%253DArch%2BGen%2BPsychiatry%26rft.aulast%253DTohen%26rft.auinit1%253DM.%26rft.volume%253D59%26rft.issue%253D1%26rft.spage%253D62%26rft.epage%253D69%26rft.atitle%253DEfficacy%2Bof%2BOlanzapine%2Bin%2BCombination%2BWith%2BValproate%2Bor%2BLithium%2Bin%2Bthe%2BTreatment%2Bof%2BMania%2Bin%2BPatients%2BPartially%2BNonresponsive%2Bto%2BValproate%2Bor%2BLithium%2BMonotherapy%26rft_id%253Dinfo%253Adoi%252F10.1001%252Farchpsyc.59.1.62%26rft_id%253Dinfo%253Apmid%252F11779284%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/archpsyc.59.1.62&link_type=DOI [3]: /lookup/external-ref?access_num=11779284&link_type=MED&atom=%2Febmental%2F5%2F3%2F89.atom [4]: /lookup/external-ref?access_num=000173115800013&link_type=ISI
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".