Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,048 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,037 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».