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Record W2945101189 · doi:10.1111/hdi.12763

Inadvertently high dialysate magnesium causing weakness and nausea in hemodialysis patients

2019· article· en· W2945101189 on OpenAlexvenueno aff
Vipul Chakurkar, Pritam S. Gade, Anil V. Godbole, Farrokh F. Wadia, Valentine Lobo

Bibliographic record

VenueHemodialysis International · 2019
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisHypermagnesemiaMedicineDialysisNauseaCreatinineWeaknessSurgeryIntensive care medicineAnesthesiaMagnesiumInternal medicineHypomagnesemia

Abstract

fetched live from OpenAlex

As maintenance hemodialysis patients are exposed to large quantities of dialysis water, any contamination of it might be reflected in plasma levels. We present a series of cases due to such a contamination. Six maintenance hemodialysis patients dialyzing at the same peripheral hemodialysis facility presented to us over a short period of time with symptoms mimicking inadequate dialysis. Their blood urea and creatinine levels were not very high, but all the patients had hypermagnesemia [serum Mg levels = 1.8 (±0.3) mmol/L]. Except for one patient who had cardiac arrest at presentation, all patients improved after undergoing hemodialysis at our center [serum Mg at discharge = 0.86 (±0.01) mmol/L]. The origin of hypermagnesemia was traced to dialysis water contamination with magnesium due to inadequate maintenance of the water treatment system. Corrective measures improved the quality of water, and no further cases were reported from that center. Proper maintenance and periodic checks of the quality of water are central to the outcomes of maintenance hemodialysis patients.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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