Loop diuretics and angiotensin converting enzyme inhibitors increased risk of hospital admission for lithium toxicity
Bibliographic record
Abstract
Juurlink DN, Mamdani MM, Kopp A, et al . Drug-induced lithium toxicity in the elderly: a population-based study. J Am Geriatr Soc 2004;52:794–8. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In older persons, is use of diuretics, angiotensin converting enzyme (ACE) inhibitors, or non-steroidal anti-inflammatory drugs (NSAIDs) associated with hospital admission for lithium toxicity? Clinical impact ratings GP/FP/Primary care ★★★★★☆☆ IM/Ambulatory care ★★★★☆☆☆ Emergency Medicine ★★★★★☆☆ Neurology ★★★★★☆☆ Geriatrics ★★★★★☆☆ ### ![Graphic][5]</img>Design: population based, nested, case control study with analysis of multiple linked healthcare databases over 10 years. ### ![Graphic][6]</img>Setting: Ontario, Canada. ### ![Graphic][7]</img>Patients: 10 615 patients ⩾66 years of age (mean age 72 y, 62% women) who were receiving uninterrupted lithium treatment and resided in Ontario, Canada. ### ![Graphic][8]</img>Assessment of risk factors: use of diuretic (alone or in combination with another agent), ACE inhibitor, or prescription NSAID (including cyclooxygenase 2 inhibitors). Thiazide type and loop diuretics were examined separately. ### ![Graphic][9]</img>Outcome: hospital admission with diagnosis of lithium toxicity within 28 days … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bthe%2BAmerican%2BGeriatrics%2BSociety%26rft.stitle%253DJ%2BAm%2BGeriatr%2BSoc%26rft.aulast%253DJuurlink%26rft.auinit1%253DD.%2BN.%26rft.volume%253D52%26rft.issue%253D5%26rft.spage%253D794%26rft.epage%253D798%26rft.atitle%253DDrug-induced%2Blithium%2Btoxicity%2Bin%2Bthe%2Belderly%253A%2Ba%2Bpopulation-based%2Bstudy.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1532-5415.2004.52221.x%26rft_id%253Dinfo%253Apmid%252F15086664%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=10.1111/j.1532-5415.2004.52221.x&link_type=DOI [3]: /lookup/external-ref?access_num=15086664&link_type=MED&atom=%2Febmed%2F10%2F1%2F28.atom [4]: /lookup/external-ref?access_num=000220855300022&link_type=ISI [5]: /embed/inline-graphic-1.gif [6]: /embed/inline-graphic-2.gif [7]: /embed/inline-graphic-3.gif [8]: /embed/inline-graphic-4.gif [9]: /embed/inline-graphic-5.gif
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".