Review: counselling, pharmacotherapy, and surgery help obese adults lose weight
Bibliographic record
Abstract
McTigue KM, Harris R, Hemphill B, et al . Screening and interventions for obesity in adults: summary of the evidence for the U S Preventive Services Task Force. Ann Intern Med 2003;139:933–49. [OpenUrl][1][PubMed][2][Web of Science][3] Q In adults, is obesity screening and treatment effective? Clinical impact ratings GP/FP/Primary care ★★★★★☆☆ IM/Ambulatory care ★★★★★☆☆ Cardiology ★★★★★☆☆ Endocrine ★★★★☆☆☆ Geriatrics ★★★★★★☆ ### ![Graphic][4]</img>Data sources: Medline, the Cochrane Library , and systematic reviews from the US, Canada, and the UK. ### ![Graphic][5]</img>Study selection and assessment: randomised controlled trials (RCTs) or systematic reviews of screening and treatment for obesity. Drug treatment studies had to have ⩾6 months of follow up; all other treatments had to have ⩾12 months of follow up. Study quality was assessed using the US Preventive Services Task Force criteria. ### ![Graphic][6]</img>Outcome: weight loss. No RCTs evaluated the efficacy of obesity screening. Counselling and behavioural interventions. 3 systematic reviews showed that behavioural interventions promoting exercise and/or dietary change achieved small weight reductions (US: 29 RCTs, mean weight change over controls −3.3 kg, range −8.8 to 1.9 at ⩾1 y) (UK: 24 RCTs, mean weight change over controls −3.0, range −10.6 to 1.4 at 12–60 mo) (Canada: 6 RCTs, mean weight change over controls −2.1, range −4.5 to −0.2 at 24–84 mo). Of … [1]: {openurl}?query=rft.jtitle%253DAnnals%2Bof%2BInternal%2BMedicine%26rft.stitle%253DANN%2BINTERN%2BMED%26rft.issn%253D0003-4819%26rft.aulast%253DMcTigue%26rft.auinit1%253DK.%2BM.%26rft.volume%253D139%26rft.issue%253D11%26rft.spage%253D933%26rft.epage%253D949%26rft.atitle%253DScreening%2Band%2BInterventions%2Bfor%2BObesity%2Bin%2BAdults%253A%2BSummary%2Bof%2Bthe%2BEvidence%2Bfor%2Bthe%2BU.S.%2BPreventive%2BServices%2BTask%2BForce%26rft_id%253Dinfo%253Apmid%252F14644897%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=14644897&link_type=MED&atom=%2Febmed%2F9%2F4%2F110.atom [3]: /lookup/external-ref?access_num=000186884800008&link_type=ISI [4]: /embed/inline-graphic-1.gif [5]: /embed/inline-graphic-2.gif [6]: /embed/inline-graphic-3.gif
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".