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Record W2966479480 · doi:10.1097/mph.0000000000001562

What Do We Know About the Do-It-Yourself Diets of Patients in Our Care?

2019· article· en· W2966479480 on OpenAlexaff
Laura Veneroni, Pietro Rossi, S. Della Valle, Cecilia Gavazzi, Cristina Meazza, Carlo Alfredo Clerici, Andrea Ferrari, Maura Massimino

Bibliographic record

VenueJournal of Pediatric Hematology/Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsMedicineFamily medicineHealth carePediatrics

Abstract

fetched live from OpenAlex

This pilot study aimed to investigate dietary changes introduced after being diagnosed with cancer in a sample of 60 consecutive children and adolescents on the basis of a self-report questionnaire. The study showed that 53% of patients changed their diet, usually "to combat the tumor" (in 47% of cases). In 1 in 2 of these cases, the health care staff were not informed about any changes. This study underscores the fact that many patients and families chose to impose dietary changes that are not always discussed with their health care staff and that may risk interfering with the continuity of a patient's treatment. It is essential to have more opportunities to speak with patients and families about this issue.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.347
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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