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Record W2120290073 · doi:10.1177/089686080102100111

Hyponatremia in Patients Undergoing CAPD: Role of Water Gain and/or Malnutrition

2001· article· en· W2120290073 on OpenAlexaff
Gonzalo Zevallos, Dimitrios G. Oreopoulos, Mitchell L. Halperin

Bibliographic record

VenuePeritoneal Dialysis International · 2001
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsToronto Western HospitalUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsHyponatremiaMedicinePeritoneal dialysisContinuous ambulatory peritoneal dialysisExtracellular fluidBody waterMalnutritionWeight lossWeight gainInternal medicinePathophysiologyEndocrinologyExtracellularBody weightObesityChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Hyponatremia has a number of different causes; some may have serious untoward implications for patients undergoing chronic ambulatory peritoneal dialysis (CAPD). OBJECTIVE: To determine the pathophysiology of hyponatremia in patients on CAPD. METHODS: A retrospective analysis was carried out on 210 patients on CAPD. We selected patients with 2-4 consecutive periods when the plasma sodium concentration was < or =130 mmol/L and again when it was > 133 mmol/L. Exclusion criteria included hyperglycemia, orthostatic hypotension, edema, and inadequate records. RESULTS: An electrolyte-free water gain appeared to be the main cause of hyponatremia in only 1 of 5 patients because this was the only patient with a significant increase in body weight. In 1 patient, there was weight loss in the hyponatremic period, suggesting tissue catabolism was present. In 3 patients, there was neither weight gain nor evidence for a contracted extracellular fluid volume in the hyponatremic period, suggesting that intracellular potassium and phosphate loss could be the major mechanism for their hyponatremia. CONCLUSION: When hyponatremia is due to a catabolic state, its management should aim to restore intracellular fluid composition (i.e., to correct malnutrition).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.424

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 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

Citations27
Published2001
Admission routes1
Has abstractyes

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