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Record W2582336877 · doi:10.1177/1753495x16686276

Composition of human breast milk in acute kidney injury

2017· article· en· W2582336877 on OpenAlex
Adam Chruscicki, A. Ross Morton, Ayub Akbari, Christine A. White

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueObstetric Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsMedicineBreast milkAcute kidney injuryBreastfeedingKidneyKidney diseaseComposition (language)Magnetic resonance imagingIodinated contrastAffect (linguistics)Computed tomographyInternal medicinePhysiologyRadiologyPathologyBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Breastfeeding is a widely encouraged practice due to its benefits for mother and the infant. Little is known about the impact of disease states, such as kidney dysfunction and childbirth complications, on the composition of breast milk. METHODS: We describe a case of a 35-year-old woman who suffered a postpartum hemorrhage, was administered a contrast dye prior to computer tomography, and developed an acute kidney injury. Using nuclear magnetic resonance spectrometry, we measured composition of milk in acute kidney injury. The amount of dye secreted into milk was determined using a spectroscopic assay. RESULTS: Here we show that acute kidney injury results in changes in milk composition, but it does not significantly affect major macronutrients. We also determine that iodinated computer tomography contrast dye does not accumulate in milk in appreciable amounts. CONCLUSION: Acute kidney injury has impact on breast milk. Intravenous administration of computer tomography contrast dye does not result in significantly elevated levels in milk.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.375
Teacher spread0.338 · 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