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Sodium and Potassium Intakes and Cardiovascular Risk Profiles in Childhood Cancer Survivors: The SCCSS-Nutrition Study

2019· preprint· en· W3124042009 on OpenAlexaff
Fabiën N. Belle, Christina Schindera, Idris Guessous, Maja Beck‐Popovic, Marc Ansari, Claudia E. Kuehni, Murielle Bochud

Bibliographic record

VenuePreprints.org · 2019
Typepreprint
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPediatric Oncology Group
FundersBundesamt für Lebensmittelsicherheit und VeterinärwesenSwiss Cancer Research FoundationCentre Hospitalier Universitaire VaudoisBundesamt für Gesundheit
KeywordsMedicineUnderweightOverweightUrineBody mass indexInternal medicine

Abstract

fetched live from OpenAlex

Risk of cardiovascular disease (CVD), common in childhood cancer survivors (CCSs), may be affected by diet. We assessed sodium (Na) and potassium (K) intake, estimated from food frequency questionnaires (FFQs) and morning urine spots, and its associations with cardiovascular risk in CCSs. We stratified CCS into three risk profiles based on A) personal history (CVD, CVD risk factors, or CVD risk free), B) body mass index (obese, overweight, or normal/underweight), and C) cardiotoxic treatment (anthracyclines and/or chest irradiation, or neither). We obtained a FFQ from 802, and sent a spot urine sample collection kit to 212, of which 111 (52%) returned. We estimated Na intake 2.9 g/day based on spot urine and 2.8 g/day based on FFQ; estimated K intake was 1.6 g/day (spot urine) and 2.7 g/day (FFQ). CCSs with CVD risk factors had a slightly higher Na intake (3.3 g/day), than CCSs risk free (2.9 g/day) or with CVD (2.7 g/day, p = 0.017), and obese participants had higher Na intake (4.2 g/day) than normal/underweight CCSs (2.7 g/day, p<0.001). Daily Na intake was above, and daily K intake below national recommended levels. Adult survivors of childhood cancer need dietary assistance to reduce Na and increase K intake.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.003
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 head0.072
GPT teacher head0.343
Teacher spread0.271 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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Same venuePreprints.orgSame topicChildhood Cancer Survivors' Quality of LifeFrench-language works237,207