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Record W2900977796 · doi:10.1155/2018/4696182

A Unique Case of Metformin-Associated Lactic Acidosis

2018· article· en· W2900977796 on OpenAlexafffund
Benjamin Gershkovich, Christopher R. McCudden, Kevin D. Burns

Bibliographic record

VenueCase Reports in Nephrology · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersOttawa Hospital Research InstituteUniversity of Ottawa
KeywordsMetforminMedicineLactic acidosisHyperkalemiaMetabolic acidosisIntensive care medicineDiabetes mellitusContext (archaeology)Type 2 diabetesAcute kidney injuryHemodialysisAcidosisType 2 Diabetes MellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Metformin-associated lactic acidosis [MALA] is a potentially fatal condition characterized by an elevation in serum lactate in patients with metformin exposure. An 82-year-old man with no prior renal history was brought to hospital after being found by his family in a confused state. He had a history of type 2 diabetes mellitus, and his medications included regular metformin. On arrival to our hospital he was conscious but confused and noted recent decreased oral intake. Initial investigations revealed severe acidemia (pH <6.75, undetectable bicarbonate), with elevated serum lactate, urea, creatinine, and hyperkalemia. He was treated with intravenous dextrose, crystalloids, and bicarbonate and underwent urgent hemodialysis. The patient responded well to supportive therapies and achieved full renal recovery one week after admission. He was discharged feeling well, with a new antihyperglycemic medication regimen. This case highlights the potential for life-threatening acidemia in cases of MALA. The case is further unique in that the patient was conscious and responded to questions on arrival, despite the serious metabolic disturbance, and recovered completely. From a safety standpoint, health care providers should advise and educate their patients about discontinuing metformin and other potentially harmful medications in the context of acute illness with volume contraction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.295
Teacher spread0.278 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations4
Published2018
Admission routes2
Has abstractyes

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