Re: Association of Cytomegalovirus (CMV) DNAemia With Long-term Mortality in a Randomized Trial of Preemptive Therapy and Antiviral Prophylaxis for Prevention of CMV Disease in High-Risk Donor Seropositive, Recipient Seronegative Liver Transplant Recipients
Bibliographic record
Abstract
To the Editor—We read with interest Kumar and colleagues’ reanalysis of the CAPSIL trial [1]. The authors seek to understand whether differences in duration of cytomegalovirus (CMV) DNAemia, associated with alternative CMV prevention strategies, impact mortality in liver transplant recipients. There are mechanistic reasons to think that CMV DNAemia might impact health outcomes in solid organ transplant recipients, even in the absence of CMV end organ disease [2]. DNAemia occurs earlier in those managed using preemptive therapy (PET) and, typically, on cessation in those taking antiviral prophylaxis (AP). Landmark analyses, as used by Kumar et al, condition on survival to landmark time (100 days post-transplant, in this study). This generates period-specific estimates of survival that are hard to interpret and cannot be generalized to the population to whom the intervention is to be offered [3, 4]. The apparent better long-term survival in CAPSIL participants randomized to PET [1] may be a chance finding. The association would be attenuated had the 5 PET (vs 1 AP) deaths occurring prior to 100 days been included. Between-group differences appear to have been driven by, presumably unrelated, deaths from malignancy and from alcohol or opiate toxicity. The association between peak CMV DNAemia and mortality in the CAPSIL trial population [1] is not from a randomized comparison. Expected confounders of the DNAemia–mortality association, such as age and degree of immunosuppression, were not included as covariates. In the general population, CMV viral load in blood (cell associated) increases with advancing age, doubling every 9.6 years [5]. Furthermore, CMV can reactivate in response to increases in immunosuppression, inflammatory stimuli, intercurrent illness, and other physiological insults [2]. We cannot tell from these data whether CMV DNAemia caused ill health, and subsequent death, or whether ill health caused CMV reactivation. In our view, either residual confounding or reverse causation could fully explain the observed association. We agree with Kumar and colleagues that “a large head-to-head trial of PET versus AP that includes long-term follow-up for mortality” would be informative [1]. Disclaimer. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health and Care Research, or the Department of Health and Social Care. Financial support. T. A. Y. is supported by the National Institute for Health and Care Research (NIHR) as an NIHR Clinical Lecturer.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.005 |
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".