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Record W1997688421 · doi:10.3148/69.1.2008.23

<i>Moderate Weight Loss:</i> A Self-directed Protocol for Women

2008· article· en· W1997688421 on OpenAlexaffvenue
Sylvia Santosa, Isabelle Demonty, Peter J.H. Jones, Alice H. Lichtenstein

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2008
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsMcGill UniversitySte. Anne's Hospital
FundersAmerican Heart Association
KeywordsWeight lossOverweightMedicinePhysical therapyObesityPhysical activityPsychological interventionGerontologyInternal medicine

Abstract

fetched live from OpenAlex

This innovative, self-directed diet and physical activity program was designed to achieve moderate weight loss in women. Thirty-five overweight or obese hyperlipidemic women completed a 20-week weight loss study. The weight loss intervention consisted of a 20% decrease in energy intake through diet and a 10% increase in energy expenditure through physical activity. The diet consisted of 50-60% carbohydrates, 20% protein, and 20-30% fat. A personal trainer prescribed physical activity regimens. A progress-tracking system and monthly group sessions were used to maintain participant motivation throughout the weight loss period. Participants lost an average of 11.7 +/- 2.5 kg (p<0.001). The pattern of weight loss was linear (p<0.001) throughout the study period. Average weight loss per week was 0.59 +/- 0.55 kg. This 20-week program, combining a structured self-selected diet and independent preplanned physical activity with motivational strategies, resulted in weight loss comparable to that observed in more controlled interventions. The lower cost, ease of use, and outcome success make this approach potentially useful in a clinical setting.

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)0.003
metaresearch head score (Gemma)0.003
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.024
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0240.004

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 head0.143
GPT teacher head0.443
Teacher spread0.301 · 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
GenreProtocol

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

Citations5
Published2008
Admission routes2
Has abstractyes

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