SERVE-HF on-treatment analysis: does the on-treatment analysis SERVE its purpose?
Bibliographic record
Abstract
The randomised clinical trial (RCT) is the best-accepted means to assess the effectiveness of a treatment for a given disease because treatment allocation is not influenced by non-random factors such as patient or physician preference. Conversely, observational trials, in which treatment allocations are not randomised, can be, and often are, subject to patient or physician preference. For this reason, the results of observational trials of various interventions are not considered to carry as much weight as those of RCTs, and results of such trials are often considered to be only suggestive or hypothesis generating, rather than definitive. Indeed, in several instances, the positive treatment results of observational trials have not been borne out by RCTs. For example, in the field of sleep apnoea and cardiovascular diseases, several non-randomised observational studies reported reduced fatal and non-fatal cardiovascular events rates among obstructive sleep apnoea (OSA) patients who elected to be treated by, and to continue on, continuous positive airway pressure (CPAP) compared to those who elected not to be treated by, or who discontinued, such treatment [1–3]. In contrast, several large-scale RCTs of treatment of OSA by CPAP demonstrated no beneficial effect of CPAP on fatal or non-fatal cardiovascular events [4–6]. Nevertheless, the reliability of the results of an RCT depends on the degree of adherence to the treatment allocation: the greater the adherence, the more reliable the results, and vice versa . For these reasons, there are various means by which RCTs can be analysed that take into account treatment adherence. Minute-ventilation triggered ASV increases mortality in heart failure patients with central sleep apnoea
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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 | 0.426 | 0.711 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.012 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 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".