Bibliographic record
Abstract
BMI is a statistical measure comparing a person's weight and height. BMI has been used by the World Health Organization as the standard for recording obesity statistics since the early 1980s (Table 1). BMI can be calculated quickly and without expensive equipment. It is defined as the individual's body weight divided by the square of his or her height (kg/m2). Due to its ease of measurement and calculation, it is the most widely used tool to estimate a healthy body weight based on a person's height. However, despite its widespread use for determining whether a person's weight is appropriate for his or her height, BMI is explicitly described (by Ancel Keys) as being appropriate for population studies, and not for individual diagnosis. As you mention, BMI is not perfect. Here are three examples that demonstrate this: For a given height, BMI is proportional to weight. However, for a given weight, BMI is inversely proportional to the square of the height. So, if all body dimensions double, and weight scales naturally with the cube of the height, then BMI doubles instead of remaining the same. So, taller people will have a BMI that is too high compared with their actual body fat levels. BMI is used to assess how much a person's body weight departs from what is desirable for a person of his or her height. However, BMI categories do not take into account many factors such as frame size and muscularity. Because BMI is dependent only on weight and height, it may overestimate adiposity in those with more lean body mass (eg, athletes) and underestimate adiposity in those with less lean body mass (eg, the elderly). Another limitation relates to loss of height through aging. In this situation, BMI will increase without any corresponding increase in weight. In children, instead of set BMI thresholds for underweight and overweight, growth is documented against a BMI-measured growth chart. The BMI percentile is used to allow comparison with children of the same sex and age. Obesity trends can be calculated from the difference between the child's BMI and the BMI on the chart, but, again, body composition is not taken into account. The choice of using the square power of height in the denominator of the formula for BMI reduces variability in the BMI associated only with a difference in size, rather than with differences in weight relative to one's ideal weight. If taller people were simply scaled-up versions of shorter people, the appropriate exponent would be 3, because weight would increase with the cube of height. The Ponderal Index is based on the natural scaling of weight with the third power of the height. However, many taller people are not just ‘scaled up’ short people; they tend to have a slimmer build relative to their height than do shorter people. An analysis based on data gathered in the United States suggested an exponent of 2.6 would yield the best fit for children aged two to 19 years. We are aware that the BMI is not perfect, and is certainly not the best tool to assess an individual. That being said, all available references are based on BMI and until references for children based on a better tool are available, BMI is still the best we have. We must remember that clinical judgment must prevail when assessing an individual.
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How this classification was reachedexpand
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.048 | 0.025 |
| Insufficient payload (model declined to judge) | 0.059 | 0.037 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".