Diabetes mellitus e cancro colorretal: uma revisão baseada na evidência
Bibliographic record
Abstract
Objective: Diabetes mellitus (DM) and colorectal cancer (CRC) are diseases with increasing incidence and high mortality. In 2014, DM accounted for 4.9 million deaths worldwide according to the International Diabetes Federation. Some studies suggest that DM is a risk factor for CRC. However, this association is inconsistent. The aim of this study was to review the evidence for the association between DM and increased incidence and mortality from CRC. Data sources: National Guideline Clearing House, NICE, Canadian Medical Association Practice Guidelines, Cochrane, PubMed, and Direção-Geral da Saúde. Methods: We searched for meta-analyses (MA), systematic reviews (SR), observational studies, and clinical guidelines based on evidence published in the last five years in Portuguese, Spanish and English, using the MeSH terms ‘diabetes mellitus’ and ‘cancer, colorectal’ and the corresponding DeCS (Portuguese search terms). We used the Strength of Recommendation Taxonomy (SORT) scale of the American Academy of Family Physicians to assign levels of evidence and strength of recommendations. Results: From the 48 articles obtained, 6 MA and 1 observational study met the inclusion criteria. Included studies found statistically significant results showing increased mortality and incidence of CRC in diabetic individuals. Four MA conducted an independent analysis by site of cancer (colon and rectum) and found a statistically significant increase in incidence and mortality of both types of cancer in diabetic population. Similarly, four MA conducted a subgroup analysis by gender showing, in most studies, a positive association between diabetes and increased incidence of CRC in both genders. Conclusion: The currently available evidence shows a statistically significant and moderate increase in incidence and mortality from CRC in diabetic individuals (SORT A). We need studies with more homogeneous methodology, regarding subtypes of DM, ethnicity, and the effects of confounding factors such as diet, obesity, smoking, sedentary lifestyle, antidiabetic medication and duration of the disease. These results are clinically relevant and may have public health implications, given the high prevalence and morbidity of DM and CRC and the possible common pathophysiological basis of these diseases.
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 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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".