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Record W2915179467 · doi:10.1111/hdi.12716

Successful management of severe hyponatremia in CKD‐VD: In a cost limited setting

2019· article· en· W2915179467 on OpenAlexvenueno aff
Navin Pattanashetti, Joyita Bharati, Harbir Singh Kohli, Krishan Lal Gupta, Raja Ramachandran

Bibliographic record

VenueHemodialysis International · 2019
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHyponatremiaMedicineAzotemiaRenal replacement therapyHemodialysisDialysisIntensive care medicineVomitingInternal medicineAnesthesiaRenal function

Abstract

fetched live from OpenAlex

Patients with end stage renal disease (ESRD) and severe hyponatremia always pose a challenge to manage. It is necessary to correct biochemical parameters, advanced azotemia, and fluid overload with conventional haemodialysis (HD) but it may correct serum sodium (Na) rapidly resulting in neurological complications like seizures and osmotic demyelination syndrome. Continuous renal replacement therapy (CRRT) is an ideal modality to manage such patients. However, most of the centers in the developing or underdeveloped nations do not have CRRT facility. We present two cases of ESRD, who had advanced azotemia requiring dialysis, also had persistent vomiting and severe hyponatremia (one with Na 107, another with Na 109 mEq/L), both cases were managed with conventional HD using dialysate Na concentration of 128 mEq/L (lowest permissible level of Na in a traditional HD machine) and keeping the blood flow of 50 mL/min. The serum Na increased by 1 mEq/L/h during first HD session, during the next session blood flow increased to 100 mL/min, and serum Na increased by two mEq/L/h. At the end of 48 hours, we were able to successfully correct serum Na by 18 mEq/L, with complete resolution of uremic manifestations and no neurological deficits. The current reports highlight management of hyponatremia in newly diagnosed ESRD in a cost limited setting.

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 score0.106
Threshold uncertainty score0.668

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)0.0010.000

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 head0.008
GPT teacher head0.263
Teacher spread0.254 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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