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Record W4400061685 · doi:10.14814/phy2.16085

Effects of 2 months of methylphenidate on energy expenditure in individuals with obesity: A randomized, <scp>double‐blind</scp>, <scp>placebo‐controlled</scp> pilot study

2024· article· en· W4400061685 on OpenAlexafffund
Kurt McInnis, Éric Doucet, Kaamel Hafizi, Fatmé El Amine, Brandon Heidinger, Jameason D. Cameron, Shakibasadat BaniFatemi, Philippe Robaey, Régis Vaillancourt, Gary S. Goldfield

Bibliographic record

VenuePhysiological Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsBell (Canada)Children's Hospital of Eastern OntarioUniversity of Ottawa
FundersChildren's Hospital of Eastern Ontario FoundationUniversity of Ottawa
KeywordsPlaceboObesityMedicineMethylphenidateEnergy expenditureResting energy expenditureWeight lossBody weightInternal medicineAttention deficit hyperactivity disorderPsychiatry

Abstract

fetched live from OpenAlex

Abstract Methylphenidate (MPH) has been previously shown to increase resting energy expenditure (REE) in individuals of normal weight; however, the effects on individuals living with obesity are currently unknown. Ten individuals living with obesity were randomly assigned to undergo 60 days of MPH administration with a daily dose of 0.5 mg/kg body weight or a placebo control. REE was measured before and after the 60‐day intervention. There was a trend toward significance for group × time interaction on REE (p = 0.082) with a large effect size (η2 = 0.331), with MPH administration increasing REE compared to a decrease in placebo control. Preliminary findings from this pilot study show that MPH has the potential to counter the adaptive thermogenic process commonly seen in weight loss. This is a unique finding among pharmacotherapies, as no approved obesity drugs measurably impact REE.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.310
Teacher spread0.284 · 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 designRandomized trial
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

Citations0
Published2024
Admission routes2
Has abstractyes

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