The incidence of delirium in older people with a mood disorder is similar with lithium and valproate
Bibliographic record
Abstract
Shulman KI, Sykora K, Gill S, et al . Incidence of delirium in older adults newly prescribed lithium or valproate: a population-based cohort study. J Clin Psychiatry 2005;66:424–7.[OpenUrl][1][PubMed][2][Web of Science][3] Q Is the incidence of delirium greater with lithium than valproate in older people with a mood disorder? ### ![Graphic][4] Design: Cohort study. ### ![Graphic][5] Setting: Ontario, Canada, from 1993 to 2001. ### ![Graphic][6] Population: 5360 people aged 66 years or older, with a mood disorder, who had not taken lithium or valproate in the previous year. Population based databases were used to identify eligible participants. People with a previous history of dementia, epilepsy, delirium, brain tumour, or schizophrenia were excluded. ### ![Graphic][7] Prognostic factors: Being prescribed lithium or valproate for a mood disorder. … [1]: {openurl}?query=rft.jtitle%253DThe%2BJournal%2Bof%2Bclinical%2Bpsychiatry%26rft.stitle%253DJ%2BClin%2BPsychiatry%26rft.aulast%253DShulman%26rft.auinit1%253DK.%2BI.%26rft.volume%253D66%26rft.issue%253D4%26rft.spage%253D424%26rft.epage%253D427%26rft.atitle%253DIncidence%2Bof%2Bdelirium%2Bin%2Bolder%2Badults%2Bnewly%2Bprescribed%2Blithium%2Bor%2Bvalproate%253A%2Ba%2Bpopulation-based%2Bcohort%2Bstudy.%26rft_id%253Dinfo%253Apmid%252F15816783%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=15816783&link_type=MED&atom=%2Febmental%2F8%2F4%2F95.atom [3]: /lookup/external-ref?access_num=000228491400003&link_type=ISI [4]: /embed/inline-graphic-1.gif [5]: /embed/inline-graphic-2.gif [6]: /embed/inline-graphic-3.gif [7]: /embed/inline-graphic-4.gif
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".