The Relationship Between Prostate Cancer Aggressiveness and Glycemic Levels in Patients Submitted to Radical Prostatectomy
Bibliographic record
Abstract
BACKGROUND: The relationship between hyperglycemia and prostate cancer remains controversial. According to current hypotheses, elevated serum glucose levels may lead to disease development or disease prevention. Our study examined the potential correlation between pre-operative glycemic levels of patients with prostate cancer and the grade of tumor aggressiveness. METHOD: We studied the case files of patients with a diagnosis of prostate cancer who had received putatively curative cancer surgery at the Urology Department of the Servidores do Estado Federal Hospital (RJ/Brazil). We transcribed information related to glycemia - collected up to 3 months before the surgery - and the histopathological grade of tumor aggressiveness (Gleason score) of the surgically removed prostates. RESULTS: We analyzed 42 people who met the inclusion criteria. Based on Gleason scores, among the normoglycemic patients, we detected low, moderate, and highly aggressive neoplasias in 13%, 53%, and 36% of the cases, respectively. For the hyperglycemic group, these rates were 30%, 60%, and 10%, respectively. Normoglycemic patients had primary Gleason grade 3 in 40% of the cases and grade 4 in 60% of the cases. For the hyperglycemic patients, these rates were 90% and 10%, respectively (P < 0.05 vs. grade 3 group). CONCLUSION: Both Gleason score and primary Gleason grade were lower in hyperglycemic patients with prostate cancer than in normoglycemic patients, suggesting a "protective action" of hyperglycemic states.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".