Bibliographic record
Abstract
Two reviews address this. The first is the result of a meeting organized by the Heart Failure Association (HFA). The roles of the Data Monitoring Committees (DMC) are outlined and recommendations regarding methodological consistency, independence, potential conflicts of interest, liability protection, and members' training are given.1 The second article describes the DMC experience during a major clinical trial, TOPCAT. An unexpectedly large incidence of deterioration of renal function was noted during the trial with a 6.1% incidence in the patients in one arm vs. 3.9% in the other (p = 0.009). This led to further assessment of the rates of drug withdrawal and adeverse events with no detection of safety concerns. The trial was therefore conducted to its end. Although the incidence of serum creatinine doubling occurred at a higher rate in the spironoactone versus the placebo arm, mortality rates after creatinine increase were lower with spironolactone (13.1% vs. 33.2%, P < 0.001).2 The effects of renin–angiotensin and aldosterone antagonists on renal function and their favorable effects on outcomes, independent of the changes in renal function, are confirmed.3,4
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".