Dialysis or not? A comparative survival study of patients over 75 years of age with chronic kidney disease stage 5
Bibliographic record
Abstract
Sir, In their article [ 1 ], Murtagh et al . carry out a retrospective, intention-to-treat analysis, comparing survival among those who elect to receive dialysis versus conservative management. Their results support the contention that patients who elect dialysis have a better survival than those who elect conservative treatment but that ‘…the survival advantage is substantially reduced by comorbidity and ischaemic heart disease in particular’. We emphasize the word elect here because, unfortunately, it is not clear from the authors' Abstract, nor from the Tables and Figure, that the results DO NOT refer to patients who actually receive dialysis but rather to patients who elect to receive dialysis. The authors are careful to point this out within the article itself but the more ‘casual’ reader should be warned that the survival curves and hazard ratios shown in the article do not reflect the actual treatment received during the course of follow-up. Indeed, an analysis reflecting the actual treatment received would require using a more sophisticated Cox- proportional hazards model with time-dependent treatment groups. Such an as-treated analysis may or may not alter the conclusions reached from the authors' intent-to-treat analysis. For example, the total number of deaths attributed to the dialysis group is mentioned as 12. However, as noted by the authors, 8 of 12 died prior to starting dialysis. Another 16 in the dialysis group never started dialysis prior to the study completion date. How were these 24 patients managed? We presume that they were given the same treatment that was offered to patients in the conservative arm before starting dialysis. In an as-treated type of analysis, the total number of patients who actually received dialysis would be 52 − 24 = 28, out of which 4 would have died following the initiation of dialysis. Likewise, at initiation, the conservative treatment arm would have started off with a total of 77 + 24 = 101 patients, of which 59 (=51 + 8) would have died while on conservative treatment. Thus, an as-treated analysis may or may not yield results and/or conclusions different from those reached by the authors. It is not that we object to the intent-to-treat approach taken by the authors, it is just that results could vary according to the type of analysis one performs and we wish to make readers aware of such a possibility. Conflict of interest statement . None declared.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".