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Record W4283448611 · doi:10.1177/14791641221111252

Accounting for concurrent antihyperglycemic medication changes in dietary and physical activity interventions: A focused literature review

2022· review· en· W4283448611 on OpenAlexaff
Dennis B. Campbell, Dana Lee Olstad, Teagan Donald

Bibliographic record

VenueDiabetes and Vascular Disease Research · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineDiabetes mellitusPsychological interventionPhysical activityEnvironmental healthIntensive care medicinePhysical therapyNursingEndocrinology

Abstract

fetched live from OpenAlex

AIMS: To summarize methods used to account for antihyperglycemic medication changes in randomized controlled trials evaluating the effect of dietary and physical activity interventions on glycemia among adults with diabetes. METHODS: Using studies included in two recently published systematic reviews of randomized controlled trials examining the glycemic effects of dietary and physical activity interventions, we evaluated how each study accounted for antihyperglycemic medication changes. Data were analyzed using summary statistics, stratified by the type of intervention studied, and each was assigned a score from 0 to 6 reflecting the strength of medication controls employed. RESULTS: < 0.001) for the dietary studies. CONCLUSIONS: We found that randomized controlled trials included in recent systematic reviews of physical activity and dietary interventions did not robustly account or control for changes in antihyperglycemic medications, with physical activity interventions doing so more robustly than dietary interventions. This is a threat to the validity of study findings, as observed glycemic changes may in fact be attributable to imbalances in concurrent medication adjustments between groups.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.439
Teacher spread0.290 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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