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] Design: population based, nested, case control study with analysis of multiple linked healthcare databases over 10 years. ### ![Graphic][6] Setting: Ontario, Canada. ### ![Graphic][7] 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] 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] 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
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
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".