Bibliographic record
Abstract
Can be achieved safely with colonoscopy screening Kidney transplantation is the only cure for progressive kidney failure that requires renal replacement by dialysis. This intervention is now very successful, but is limited by increased mortality associated with infection, cardiovascular disease, and cancer. One important cancer related cause of death in this population is colorectal cancer. In a linked research paper (doi:10.1136/bmj.e4657), Collins and colleagues study the prevalence of this cancer in kidney transplant recipients aged over 50 years and the diagnostic accuracy of colonoscopy screening in this population.1 Although deaths from colorectal cancer are starting to fall in the general population, probably because of greater public awareness and more systematic screening,2 kidney transplant recipients have a twofold increased risk of de novo colorectal cancer.3 4 5 These patients are often younger at diagnosis than those in the general population. Their five year survival rate was also significantly lower than for other patients with colorectal cancer in an observational study that used the National Cancer Institute Survival, Epidemiology and End Result (SEER) database.6 This worse prognosis is probably related to increased tumour aggressiveness, reduced …
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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".