Explaining the Obesity Paradox—Response
Notice bibliographique
Résumé
We appreciate the letter of Winkels and colleagues (1) who, with two pertinent points, underscore the importance of providing data that will allow readers to compare our results with those from other studies. First, as noted in their letter, 17% of our patients had their CT scans after colorectal cancer surgery, which could affect their body composition. Even though all scans were taken before administration of chemotherapy, we agree that sensitivity analyses of only patients who had CT scans before surgery would provide the best estimate of at-diagnosis body composition. Our analyses of those patients (n = 2,701) whose CT scan was administered before surgery found HRs for the effect of sarcopenia on both overall and colorectal cancer mortality were almost identical to those in the larger patient population reported in our original article (2). Including only presurgery CT scans, the HR for sarcopenia and overall mortality was HR = 1.21 [95% confidence interval (CI), 1.02–1.42] compared with HR = 1.27 (95% CI, 1.09–1.48) reported in our article, and for sarcopenia and colorectal cancer–specific mortality, the HR was 1.41 (95% CI, 1.14–1.75) compared with HR = 1.46 (95% CI, 1.19–1.79) reported in our article. Other results reported in Table 2 were similar as well.Second, in response to Winkels and colleagues' question on our use of muscle as a variable, we chose to present skeletal muscle area at the L3 in tertiles, with simultaneous adjustment for body mass index (BMI), a measure of body size, as a demonstration that absolute muscle area is also an important predictor of survival. As requested, we reanalyzed the data in Table 2 and Fig. 3 using SMI (skeletal muscle index), substituting sarcopenia to define low muscle, instead of the lowest tertile of absolute muscle area. Again, results were almost identical. In Fig. 3, when sarcopenia was used to define low muscle instead of absolute muscle area, 57% of those with a BMI between 25 and 30 kg/m2 were considered normal (neither high adiposity or low muscle) compared with 58.6% reported in our article. Similarly, in the phenotype analyses (Table 2), effects for low muscle, or low muscle and high adiposity, on overall mortality defined by sarcopenia were similar, even slightly stronger with HR = 1.42 (95% CI, 1.18–1.70) and HR = 1.49 (95% CI, 1.16–1.92) respectively, compared with HR = 1.33 (95% CI, 1.10–1.61) and HR = 1.40 (95% CI, 1.03–1.90) when low muscle was defined by absolute muscle area.See the original Letter to the Editor, p. 1575No potential conflicts of interest were disclosed.This work was supported by grant CA175011 [Body Composition, Weight, and Colon Cancer Survival; to B.J. Caan (principal investigator)].
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 | 0,009 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,037 | 0,050 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
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 ».