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Record W3006959757 · doi:10.3148/cjdpr-2020-003

How Food Processing Impacts Hyperkalemia and Hyperphosphatemia Management in Chronic Kidney Disease

2020· article· en· W3006959757 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2020
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersCanadian Institutes of Health Research
KeywordsFood processingHyperkalemiaHyperphosphatemiaSodiumKidney diseaseFood sciencePotassiumNutrientMedicinePhosphorusFood additiveChemistryInternal medicine

Abstract

fetched live from OpenAlex

Food processing has a unique impact on patients living with chronic kidney disease who may need to restrict dietary sodium, potassium, and phosphorus intake. Canada is the second largest consumer of processed food in the world. Highly processed foods tend to be more nutrient dense, contain less fibre, and are higher in sodium than unprocessed foods. To reduce the amount of sodium in processed food, Health Canada has encouraged food producers to reduce the sodium in their food. Potassium additives have been identified as an attractive alternative to sodium and their use in food processing is expected to increase. Phosphorus additives have been reported to be present in about 44% of processed foods. Given the changes in the nutrient profiles of processed foods, dietary advice on ways to reduce sodium, potassium, and phosphorus intake may be best achieved by recommending minimally processed food and encouraging unprocessed foods more often.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.049
GPT teacher head0.340
Teacher spread0.291 · 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