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Record W2761635426 · doi:10.1158/1055-9965.epi-17-0613

Explaining the Obesity Paradox—Response

2017· letter· en· W2761635426 on OpenAlexaff
Bette J. Caan, Jeffrey A. Meyerhardt, Carla M. Prado

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2017
Typeletter
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsSarcopeniaMedicineConfidence intervalColorectal cancerSkeletal muscleBody mass indexObesityPopulationCancerInternal medicineSurgery

Abstract

fetched live from OpenAlex

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)].

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.006
Open science0.0050.003
Research integrity0.0370.050
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.174
GPT teacher head0.455
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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