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Record W3164005169 · doi:10.1136/bmj.n262

Low and very low carbohydrate diets for diabetes remission

2021· letter· en· W3164005169 on OpenAlexaff
Joshua Z. Goldenberg, Bradley C. Johnston

Bibliographic record

VenueBMJ · 2021
Typeletter
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsDiabetes mellitusCarbohydrateMedicineRandomized controlled trialPsychological interventionLow carbohydrateAdverse effectInternal medicineEndocrinologyWeight lossObesityNursing

Abstract

fetched live from OpenAlex

Dietary interventions that restrict carbohydrate intake for the management of diabetes are of particular interest to researchers, healthcare providers, and patients. Based on evidence of moderate to low certainty from 23 randomized trials (n=1357), evidence synthesis suggests that patients who adhere to low or very low carbohydrate diets for six months might achieve diabetes remission without adverse consequences. But the definition of low and very low carbohydrate diets, the long term health effects of carbohydrate restricted diets, and the working definitions of diabetes remission are debated, requiring further investigation, particularly for longer term health outcomes based on evidence from randomized trials.

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)0.005
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0240.018
Insufficient payload (model declined to judge)0.0100.006

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 head0.022
GPT teacher head0.282
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations15
Published2021
Admission routes1
Has abstractyes

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