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Record W2965125435 · doi:10.1111/obr.12910

Low capacity to oxidize fat and body weight

2019· review· en· W2965125435 on OpenAlexaff
François Péronnet, François Haman

Bibliographic record

VenueObesity Reviews · 2019
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of OttawaUniversité de Montréal
Fundersnot available
KeywordsRespiratory quotientObesityEnergy balanceBalance (ability)Weight gainBody weightGlycogenQuotientMathematicsEndocrinologyMedicineBiologyPhysical therapyPure mathematics

Abstract

fetched live from OpenAlex

For a given positive energy balance, a low capacity to oxidize fat could contribute to weight gain (low fat oxidation hypothesis). This hypothesis is based on the arguments that for a given stable diet and food quotient (FQ), the respiratory quotient (RQ) is higher in obesity prone (OP) than in obesity resistant individuals (OR) and that a high RQ predicts higher future weight gain. A review of 42 studies shows that there is no convincing experimental support to these arguments and thus for the low fat oxidation hypothesis. A power analysis also shows that this hypothesis might be impossible to experimentally confirm because very large numbers of subjects would be needed to reject the null hypotheses that the 24-h RQ is not different in OP and OR or that future weight gain is not different in individuals with a low and high 24-h RQ at baseline. A re-examination of the significance of the 24-hour and fasting RQ also shows that the assumption underlying the low fat oxidation hypothesis that a high RQ reflects a low capacity to oxidize fat is not valid: For a stable diet, the 24-h RQ entirely depends on FQ and energy balance, and the fasting RQ mainly depends on the FQ and energy balance and on the size of glycogen stores.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.084
GPT teacher head0.347
Teacher spread0.262 · 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
GenreReview

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

Citations19
Published2019
Admission routes1
Has abstractyes

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