Bibliographic record
Abstract
Sir, We thank Dr Savica et al. for their interesting data. We agree that these statistically significant results are striking for such a small sample size ( n = 16) and the findings are very different from those observed in the randomized DCOR study. We agree that the interesting results of Savica et al. should be confirmed in a much larger randomized trial. We thank Dr Wrong and Dr Harland for their comments. We agree that there are several other promising candidate medications for use as phosphate binders in people with kidney failure and that colestipol is one such agent. However, colestipol would need to be studied in properly done randomized trials before its use could be recommended. Although available evidence does not support the widespread use of sevelamer on either clinical or economic grounds, the theoretical rationale for the use of non-calcium-based binders remains compelling. We hope that the lessons learned from the DCOR, the RIND and other studies will stimulate the design and execution of new trials that determine whether sevelamer (or other non-calcium containing phosphate binders) have clinically meaningful effects—focusing on populations that may be likely to benefit. Until such time, it is very difficult to justify the use of sevelamer, especially in publicly funded health care systems. Conflict of interest statement. None declared.
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.037 | 0.036 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".