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A case of Conn's syndrome revealed after oral sodium phosphate (fleet) preparation for colonoscopy

2010· article· en· W2064537295 on OpenAlexaffabout
Ralph Lee, Martin Storr, N.B. Hershfield

Bibliographic record

VenueJournal of Digestive Diseases · 2010
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineHypokalemiaHyperphosphatemiaPrimary aldosteronismColonoscopyAldosteroneHyperaldosteronismHypernatremiaGastroenterologyInternal medicineAdenomaEndocrinologySodiumUrologyKidney diseaseColorectal cancerCancer

Abstract

fetched live from OpenAlex

Oral sodium phosphate solutions (Fleet Mcneil Consumer Healthcare, Guelph, Ontario, Canada) are commonly used as bowel cleansing agents in preparation for colonoscopic exams; however, serious electrolyte disorders associated with oral sodium phosphate use have been described in case reports including hyperphosphatemia, hypocalcemia, hypomagnesemia, hypernatremia, and hypokalemia. We describe a 57-year-old patient with a past history of resistant hypertension who experienced severe symptomatic hypokalemia following colonic cleansing with an oral sodium phosphate solution. Further investigations revealed a serum aldosterone of 691 pmol/L and a serum renin level of 0.02 ng/L/s with a corresponding aldosterone-to-renin ration of 34550:1. The patient was subsequently diagnosed with primary aldosteronism secondary to an adenoma of the adrenal gland. Bilateral adrenal venous sampling revealed excessive levels of aldosterone in the left adrenal vein prior to definitive surgery. This case indicates that an oral sodium phosphate bowel preparation, though safe for most patients, can be complicated by a previously not diagnosed endocrine disease like the primary aldosteronism (Conn's syndrome) reported here. This is the first report of a Conn's syndrome diagnosed after bowel cleansing with a sodium phosphate solution.

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)0.001
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 score0.120
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.299
Teacher spread0.290 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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