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Record W2128463281 · doi:10.1136/ebmh.7.4.120

Concomitant loop diuretics and ACE inhibitors increase risk of lithium toxicity in elderly people

2004· letter· en· W2128463281 on OpenAlexaboutno aff
Robin Jacoby

Bibliographic record

VenueEvidence-Based Mental Health · 2004
Typeletter
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLithium (medication)MedicineConcomitantInternal medicine

Abstract

fetched live from OpenAlex

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 elderly people, is lithium toxicity associated with concomitant use of diuretics, angiotensin converting enzyme inhibitors, or nonsteroidal anti-inflammatory drugs? ### ![Graphic][5]</img>Design: Nested case control study. ### ![Graphic][6]</img>Follow up period: 10 years (January 1992 to December 2001). ### ![Graphic][7]</img>Setting: Analysis of linked healthcare databases, Ontario, Canada. ### ![Graphic][8]</img>People: 10 615 people, aged ⩾66 years (62% women), treated continuously with lithium. Four controls, matched for age, sex, and lithium use were selected (randomly where more than four were identified). ### ![Graphic][9]</img>Risk factors: Diuretics, including thiazide-type (such as chlorthalidone) and loop diuretics (such as furosemide), angiotensin converting enzyme (ACE) inhibitors or non-steroidal anti-inflammatory … [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=%2Febmental%2F7%2F4%2F120.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

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.293
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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