MétaCan
Menu
Back to cohort
Record W1600174520 · doi:10.82308/3458

The effect of lipid-lowering pharmacotherapy on concurrent diet and exercise behaviors /

2000· book· en· W1600174520 on OpenAlexfundno aff
Heidi Staples

Bibliographic record

VenueeScholarship@McGill (McGill) · 2000
Typebook
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsnot available
FundersMcGill University
KeywordsPharmacotherapyPsychological interventionNational Cholesterol Education ProgramMedicinePhysical therapyPharmacologyInternal medicinePsychiatryObesityMetabolic syndrome

Abstract

fetched live from OpenAlex

The National Cholesterol Education Program Adult Treatment Panel II (NCEP ATP II) unequivocally advocates an initial trial of dietary modification in both primary and secondary prevention prior to the institution of pharmacotherapy. Perhaps the rationale for this delay rests in the inherent, yet unsubstantiated, fear among clinicians that lifestyle change will be compromised in the presence of concurrent pharmacotherapy. However, the question of adherence to diet and exercise interventions following the initiation of lipid-lowering drug therapy has seemingly never been addressed scientifically. It was therefore hypothesized that pharmacologically-treated patients with untreated hypercholesterolemia started on a program of lifestyle modification would achieve relatively less reduction in dietary fat intake and body weight, and participate less often in physical activity, if a pharmacologic agent was simultaneously prescribed. This was tested by a protocol in which these and related variables were assessed in participants who thought they were taking a lipid-lowering medication at diagnosis, compared to conventional initial treatment of diet and exercise alone. (Abstract shortened by UMI.)

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0350.006

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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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 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

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicPharmacology and Obesity TreatmentFrench-language works237,207