An extra year of Onco-Nephrology fellowship training is required for the subspecialty: PRO
Bibliographic record
Abstract
Recent advances in the fields of hematology and oncology over the past decades are truly astounding. Higher response rates have increased the overall and progression free survival, and for certain cancers, have brought the possibility of a cure within reach. While this represents great news for many cancer patients, their longer survival is creating new challenges. Cancer patients can now live long enough to develop chronic kidney disease (CKD), receive long term dialysis, and even a kidney transplantation. There are important and significant knowledge gaps in the care of the cancer patient with CKD or end stage renal disease (ESRD). The subspecialty of Onco-Nephrology was created with the goal of optimizing the care of cancer patients with CKD or ESRD by (1) identifying crucial knowledge gaps in the proper care of these patients by (2) engaging in basic science and clinical research independently and in partnership with cancer specialists to attenuate these gaps, and (3) by educating future onco-nephrologists.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.121 | 0.053 |
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".