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Record W4297021578 · doi:10.1002/oby.23530

Association between weight‐loss history and weight loss achieved in clinical obesity management: Retrospective chart review

2022· article· en· W4297021578 on OpenAlexaff
Jennifer L. Kuk, Elham Kamran, Sean Wharton

Bibliographic record

VenueObesity · 2022
Typearticle
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsHamilton Medical Research GroupYork University
Fundersnot available
KeywordsWeight lossOverweightMedicineObesityWeight managementManagement of obesityWeight changeRetrospective cohort studyMedical recordPediatricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Weight history and its association with the weight loss achieved in a publicly funded clinical obesity management program were examined in 9348 patients. METHODS: Weight history (frequency and magnitude of weight losses) was collected through questionnaires at enrollment, and weight change was assessed with retrospective electronic medical chart review. RESULTS: The majority of patients reported developing overweight prior to the age of 40 years and having lost at least 4.5 kg (10 lb) of weight at least once in their lifetime. Those who had an earlier onset of overweight had a higher frequency of past weight loss and had more cumulative weight loss over their lifetime. In women, but not men, earlier age of overweight onset and lifetime weight loss were associated with modestly greater weight loss at the clinic. CONCLUSIONS: Women with greater weight-loss history also have modestly greater weight loss at the obesity management clinic. Thus, successful long-term obesity management, particularly for women, may include a series of repeated attempts at weight loss that should not be viewed as failures but could be viewed instead as practice.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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