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Record W1985094449 · doi:10.1038/oby.2007.45

Methylphenidate Hydrochloride Increases Energy Expenditure in Healthy Adults

2008· article· en· W1985094449 on OpenAlexafffund
Claudio Lorello, Gary S. Goldfield, Éric Doucet

Bibliographic record

VenueObesity · 2008
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsPlaceboMedicineCrossover studyHeart rateBlood pressurePostprandialResting energy expenditureRespiratory exchange ratioEnergy expenditureInternal medicineVital signsAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the effects of methylphenidate hydrochloride (MPH) on resting energy expenditure (REE) and postprandial energy expenditure (PEE) and substrate partitioning. METHODS AND PROCEDURES: Seven healthy men and seven healthy women participated in this double-blind, randomized, placebo-controlled, crossover study. MPH (0.5 mg/kg) or placebo was administered orally in the fasting state, 60 min before a REE measurement, and 90 min before a standardized breakfast of approximately 650 kcal. REE, PEE, and respiratory exchange ratio (RER) were obtained from indirect calorimetry. Body composition was measured using DEXA. Vital signs (blood pressure (BP) and heart rate (HR)) were assessed pre- and post-administration of MPH or placebo in every session. RESULTS: During the, MPH condition, REE increased over values observed during the placebo session (7%, P < 0.001). No changes in fasting RER were noted. Although PEE continually decreased with time as expected, MPH treatment resulted in significantly greater PEE values at 90 min (5%, P < 0.01). No significant effects of MPH were found for vital signs (HR, systolic, and diastolic BP). DISCUSSION: MPH causes a significant increase in both REE and PEE without the significant changes in HR and BP that are commonly associated with psychostimulant use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.288
Teacher spread0.263 · 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.

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

Citations20
Published2008
Admission routes2
Has abstractyes

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