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Record W1805389091 · doi:10.1177/089686080102100103

A Physiological Analysis of Hyponatremia: Implications for Patients on Peritoneal Dialysis

2001· editorial· en· W1805389091 on OpenAlexaff
David Z.I. Cherney, Gonzalo Zevallos, Dimitrios G. Oreopoulos, Mitchell L. Halperin

Bibliographic record

VenuePeritoneal Dialysis International · 2001
Typeeditorial
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsUniversity Health NetworkUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsHyponatremiaExtracellular fluidPeritoneal dialysisMedicineFree water clearanceInternal medicineEndocrinologyVasopressinRenal functionFluid compartmentsBody waterMannitolChemistryExtracellularBody weightBiochemistry

Abstract

fetched live from OpenAlex

The basis for hyponatremia is a negative balance for sodium (Na+) plus potassium (K+) and/or a positive balance for water. In patients with normal renal function, vasopressin is needed to prevent the excretion of electrolyte-free water. Vasopressin is not important when there is little residual renal function. If hyponatremia is accompanied by a quantitatively appropriate gain in weight, this implies that a gain of electrolyte-free water was the basis for hyponatremia. In the absence of this weight gain, a loss of salts is to be suspected. If the extracellular fluid (ECF) volume is obviously low, hyponatremia is due to a deficit of NaCl, unless there is a deficit of K+. With a KCl deficit and a contracted ECF volume, there should also be a large shift of Na+ into cells, so metabolic alkalosis would not be an expected finding. In contrast, those patients with no change in weight who have a normal or expanded ECF volume are subdivided into those with a gain of solutes restricted to the ECF compartment (glucose, mannitol), or those with a deficit of solutes of intracellular fluid origin, which implies that a catabolic state (malnutrition) may be present.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.325
Teacher spread0.308 · 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 designNot applicable
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

Citations31
Published2001
Admission routes1
Has abstractyes

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