A Retrospective Survey of Patients Undergoing Maintenance Hemodialysis vis-à-vis Cancer Prevalence
Bibliographic record
Abstract
The present study investigated the cancer prevalence and anticancer treatment patients undergoing hemodialysis at Nagoya Memorial Hospital. We retrospectively analyzed 663 patients undergoing hemodialysis between September 2014 and August 2019, including patient characteristics such as age, sex, and underlying diseases, cancer type, and cancer treatment. Seventy-eight patients (11.9%) of the dialysis population were diagnosed with cancer. Cancer type was then compared between registered cancer patients undergoing maintenance dialysis (N = 78) and non-dialysis controls (N = 3279) during the same period. Colorectal carcinoma is the most common malignancy diagnosed in our hospital, accounting for approximately 15% of all types of cancers. The data of anticancer treatment for this disease were compared between dialysis patients (N = 15) and controls (N = 563), whose clinical stages were defined according to the Japanese Classification of Colorectal, Appendiceal, and Anal Carcinoma. Since the need to administer chemotherapeutic agents to dialysis patients with colorectal carcinoma will increase, oncologists should collaborate with nephrologists to cautiously manage anticancer treatment to avoid severe toxicities.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".