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Record W2060338667 · doi:10.1177/0891988712436690

The Effects of Lithium on Renal Function in Older Adults—A Systematic Review

2012· review· en· W2060338667 on OpenAlexaff
Soham Rej, Nathan Herrmann, Kenneth I. Shulman

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2012
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences CentreMcGill University
Fundersnot available
KeywordsNephrogenic diabetes insipidusPolyuriaLithium (medication)MedicineUrine osmolalityRenal functionInternal medicineDiabetes insipidusAdverse effectIncidence (geometry)EndocrinologyDiureticDiuresisUrologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Chronic renal failure (CRF) and nephrogenic diabetes insipidus (NDI) are potential consequences of chronic lithium use, while acute renal failure (ARF) has been described in lithium intoxication. We performed a systematic review of all studies pertaining to the effects of lithium on the kidney in older adults. The ARF incidence was 1.5% per person-year and concurrent loop diuretic and angiotensin-converting enzyme inhibitor use with lithium increased the risk. The CRF prevalence estimates varied from 1.2% to 34%, with risk factors including age, previous lithium intoxication, polyuria, previously impaired renal function, and decreased maximal urine osmolality. The prevalence of NDI varied widely from 1.8% to 85%. Risk factors included lithium duration, dose, level, slow-release formulation, and clinical nonresponse. Except for amiloride use in NDI, there is little evidence for treatment of other lithium-induced adverse renal effects. Currently, there is no compelling evidence to suggest that lithium should be avoided in elderly patients for fear of renal side effects.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations81
Published2012
Admission routes1
Has abstractyes

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