Rationing renal replacement therapy to older patients-agreed guidelines are needed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It has been predicted that the requirement for renal replacement therapy in the U.K. will increase by 50–100% within 15 years.1 This therapy is an expensive resource2 and unlimited access to treatment will prove difficult to fund. The number of persons aged >65 years is increasing in all developed countries, and this trend is projected to continue into the future.3 There is a steep rise in the incidence of end‐stage renal failure with age,4,,5 and much of the aforementioned rise in the requirement for renal replacement therapy relates to these demographic changes. Increased acceptance of older patients for replacement therapy in the UK is evidenced by a rise in the proportion of older persons (>65 years) from 11% to 41% between 1982 and 1995.6 Nonetheless, a huge disparity in overall acceptance rates persists between the UK, Canada and the US. In England and Wales, the annual acceptance rates rose from 67 per million population (pmp) in 1982 to 82 in 1995.6 This compares poorly with 98 and 212 pmp in Canada7 and the US,8 respectively. Some (but not all) of this disparity is explained by a higher true incidence of end‐stage renal disease in the US and Canada.7,,8 Whether optimal access to renal replacement therapy is available to older patients in any of these jurisdictions is unclear. It could be that such therapy is excessively or inappropriately available to older patients in the US, without consideration of the likelihood of health or social gain to the individual patient. Alternatively, are older patients with renal failure being denied access to worthwhile treatment in the UK solely on the basis of their chronological age? A …
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it