Association Between Obesity and Cancer Mortality: An Internal Medicine Outpatient Clinic Perspective
Bibliographic record
Abstract
Background: Obesity is one of the leading preventable causes of cancer that has a causal relationship with cancers of esophagus, breast and colon. Paradoxically, there are studies demonstrating that obesity is associated with improved survival in cancer patients. The aim of our study was to investigate the association of obesity and cancer mortality in adult patients. Methods: Retrospective medical record review of 784 adult patients was performed who had a diagnosis of cancer and who were seen in our outpatient Internal Medicine Clinic between January 1, 2019 and December 31, 2019. Results: Forty-three (5.2%) patients were cancer non-survivors and 741 (94.8%) were cancer survivors. The mean age of the cancer non-survivors group was significantly higher than that of the cancer survivors (78.7 vs. 68.0 years, respectively; P < 0.001). For every unit increase in age, there was 7.6% increased odds of cancer death (95% confidence interval (CI): 3-12%) (P = 0.001). Average body mass index (BMI) of the patients in the cancer non-survivors group was significantly lower than that of the cancer survivors group (25.0 vs. 28.1 kg/m 2 ; P = 0.008). Non-obese patients had 4.9 times greater odds of cancer death (95% CI: 1.51 - 15.81) (P = 0.008). The mean glycosylated hemoglobin (HbA1c) was significantly higher in the cancer non-survivors group compared to the cancer survivors group (7.1% vs. 6.0%; P < 0.001), and for every unit increase in HbA1c there was 1.6 times greater odds of cancer death (95% CI: 1.14 - 2.23) (P = 0.006). Patients with peripheral artery disease (PAD) had 3.5 times greater odds of cancer death compared to those without PAD (95% CI: 1.18 - 10.19) (P = 0.023). Conclusions: Non-obese patients with cancer had higher odds of cancer death. Rising HbA1c, increasing age, and presence of PAD were associated with increased cancer mortality. J Clin Med Res. 2021;13(7):377-386 doi: https://doi.org/10.14740/jocmr4543
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How this classification was reachedexpand
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".