BMI, waist circumference and fat composition are not correlated with mortality risk in an older Korean population, but higher lean mass and lean mass index are predictors of reduced mortality risk
Bibliographic record
Abstract
Commentary on: 1. Han SS, 2. Kim KW, 3. Kim KI, 4. et al . Lean mass index: a better predictor of mortality than body mass index in elderly Asians. J Am Geriatr Soc 2010;58:312–17. [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Obesity is at epidemic proportions in all age groups, including the older people. For instance, in USA approximately 35% of older adults are obese as determined by the body mass index (BMI, weight in kg/height in m2).1 Obesity is a risk factor for several chronic diseases and premature mortality.2 However, the effect of high BMI on mortality is less pronounced in the older people.3 The purpose of the study by Han and colleagues was to examine the effect of BMI, other obesity measures (body fat, waist circumference) and lean body mass on mortality risk. They examined 877 Koreans aged 65 and older. Participants were divided into three groups (<25th, 25–75th and >75th percentile) for each body composition measure and followed over … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Bthe%2BAmerican%2BGeriatrics%2BSociety%26rft.stitle%253DJ%2BAm%2BGeriatr%2BSoc%26rft.aulast%253DHan%26rft.auinit1%253DS.%2BS.%26rft.volume%253D58%26rft.issue%253D2%26rft.spage%253D312%26rft.epage%253D317%26rft.atitle%253DLean%2Bmass%2Bindex%253A%2Ba%2Bbetter%2Bpredictor%2Bof%2Bmortality%2Bthan%2Bbody%2Bmass%2Bindex%2Bin%2Belderly%2BAsians.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1532-5415.2009.02672.x%26rft_id%253Dinfo%253Apmid%252F20070416%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=10.1111/j.1532-5415.2009.02672.x&link_type=DOI [3]: /lookup/external-ref?access_num=20070416&link_type=MED&atom=%2Febmed%2F15%2F4%2F125.atom [4]: /lookup/external-ref?access_num=000274183800013&link_type=ISI
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: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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.
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".