Bibliographic record
Abstract
People undergoing dialysis have a substantially shortened survival and a high rate of cardiovascular events. Recent clinical trials have failed to demonstrate a survival benefit of reducing traditional or dialysis-specific cardiovascular risk factors. The reasons for the failure of these clinical trials are unclear. One candidate explanation is that they lacked sufficient statistical power to detect important outcome differences. Several errors in trial design or execution can lead to trials being underpowered. An overestimation of the attributable risk of the condition of interest is a common error. Statistical models that partition attributable risk can be invalid when multiple risk factors are present, as is the case in dialysis patients. Diabetes investigators faced similar challenges in their early clinical trials. However, using only 160 people with diabetes, the Steno-2 Study demonstrated a 50% reduction in cardiovascular risk in people. This impressive result was achieved because patients in the experimental arm of the Steno-2 Study underwent reduction of multiple cardiovascular risk factors simultaneously. A Steno-2 Study approach would be an attractive trial design for dialysis investigators. It could be done with fewer resources than conventional randomized trials, and a positive result would strongly argue against therapeutic complacency in the dialysis unit. However, a variety of limitations to this approach exist. Most significantly, if positive, it would not be possible to determine which individual components of the intervention led to the improved outcomes. Despite the limitations, the Steno-2 design should currently be an attractive option for dialysis investigators.
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 | 0.258 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".