Bibliographic record
Abstract
Sir, With respect, I believe that Drs Olgaard and Lewin have missed the point of Palmer's paper and the accompanying Editorial. Current use of activated vitamin D in contemporary haemodialysis patients is not restricted to dramatic examples of clinically evident deficiency like the child presented in their figure. Rather, a substantial proportion of adult patients with kidney failure receive activated vitamin D, especially in jurisdictions where reimbursement policies encourage its use. There is no doubt that Palmer's meta-analysis has methodological limitations, which they acknowledge in their paper. To dismiss the significance of their work because of these limitations is to miss the forest for the trees. Whether activated vitamin D is ultimately proven (or not) to improve patient outcomes in the future is not the point. The issue is that (as shown by the carefully done meta-analysis) there is very little evidence to support current prescription patterns for vitamin D in haemodialysis patients. More the pity, nephrologists continue to use and advocate for such unproven therapies in the absence of good quality evidence. Olgaard and Lewin compare activated vitamin D therapy in haemodialysis patients with insulin treatment for type 1 diabetics, and imply that evidence from randomized controlled trials is unnecessary to evaluate the merits of either. This comparison is spurious. It is erroneous to suggest (as Olgard and Lewin do) that ‘substitution of a missing hormone’ will always be beneficial. Although the practice of medicine would certainly be simpler if this were true, reality is more complex. This truth is exemplified by the case of hormone replacement therapy for post-menopausal women, which was supported by a vast body of theoretical, experimental and epidemiological data, but which was subsequently shown to be harmful in well-designed randomized trials. Unfortunately, thousands of women were injured and millions of dollars wasted due to this well-meaning error. Will vitamin D prove to be similarly harmful? Or remarkably helpful? We do not know. And until we do more trials, both possibilities remain. Olgaard and Lewin characterize my Editorial as ‘potentially dangerous’. I believe that the real danger lies not in calling for randomized trials to inform clinical practice, but rather in failing to learn from the lessons of the past. History teaches that we do not understand physiology well enough to determine from first principles which treatments will be beneficial. To suggest that only ‘hospital administrators’ should insist on evidence rather than anecdote to guide treatment is a serious misconception. If physicians choose which therapies to prescribe based on theoretical considerations alone, we will be wrong more often than not. Funds that are squandered in this way will be unavailable for use on novel, indisputably beneficial therapies, which grow more expensive every year. Worse (like our historical colleagues who used their understanding of physiology to justify the prescription of arsenic for syphilis), we run the risk of causing harm despite our best intentions. Well-designed randomized studies are costly, challenging to complete and have their own limitations. However, they are the best option available. Rather than pretending that we know the answers before the evidence is in, the nephrology community should get on with designing and executing more randomized trials. Conflict of interest statement . Dr. Tonelli holds peer-reviewed research funding from the Centre for D-Receptor Activation Research to examine vitamin D status in remote-dwelling patients on dialysis.
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".