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Record W2317125450 · doi:10.1139/apnm-2013-0322

Developing a Web-based dietary sodium screening tool for personalized assessment and feedback

2013· article· en· W2317125450 on OpenAlexafffundvenueabout
JoAnne Arcand, Kasim E. Abdulaziz, Carol Bennett, Mary R. L’Abbé, Douglas G. Manuel

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSodiumCalculatorMedicinePublic healthPopulationEnvironmental healthComputer scienceChemistry

Abstract

fetched live from OpenAlex

Dietary sodium reduction is commonly used in the treatment of hypertension, heart and liver failure, and chronic kidney disease. Sodium reduction is also an important public health problem since most of the Canadian population consumes sodium in excess of their daily requirements. Lack of awareness about the amount of sodium consumed and the sources of sodium in diet is common, and undoubtedly a major contributor to excess sodium consumption. There are few known tools available to screen and provide personalized information about sodium in the diet. Therefore, we developed a Web-based sodium intake screening tool called the Salt Calculator ( www.projectbiglife.ca ), which is publicly available for individuals to assess the amount and sources of sodium in their diet. The Calculator contains 23 questions focusing on restaurant foods, packaged foods, and added salt. Questions were developed using sodium consumption data from the Canadian Community Health Survey cycle 2.2 and up-to-date information on sodium levels in packaged and restaurant food databases from the University of Toronto. The Calculator translates existing knowledge about dietary sodium into a tool that can be accessed by the public as well as integrated into clinical practice to address the high levels of sodium presently in the Canadian diet.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.838

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)0.0000.000

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 head0.041
GPT teacher head0.314
Teacher spread0.273 · 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 designBench or experimental
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

Citations27
Published2013
Admission routes4
Has abstractyes

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