Reporting of Lost to Follow-Up and Treatment Discontinuation in Pharmacotherapy and Device Trials in Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Premature treatment discontinuation and loss to follow-up (LTFU) with unknown outcomes leave uncertainty about the true efficacy and safety of a treatment and a lack of confidence in the results of any trial. We reviewed the extent of (and trends over time in) reporting LTFU and treatment discontinuation in large studies in chronic heart failure published since 1990. METHODS AND RESULTS: Online databases were systematically reviewed to identify randomized controlled clinical trials (RCTs) in chronic heart failure with >400 participants and utilizing all-cause mortality as a component of the primary or secondary end point. Assessments were made of documentation of treatment discontinuation, LTFU, inclusion of and completeness of a Consolidated Standards Of Reporting Trials (CONSORT) diagram, and whether LTFU was differentiated from withdrawal of consent. Sixty-eight trials were identified, with >154 000 participants. Reasons for treatment discontinuation in pharmacotherapy trials were infrequently reported (35%), particularly in a CONSORT diagram (20%). Eighty-three percent of trials reported LTFU, although only 34% of these differentiated LTFU for vital status from withdrawal of consent. Use of a CONSORT diagram increased over time, although reporting of LTFU in the CONSORT diagram remained low overall at 35%. CONCLUSIONS: Participant flow through RCTs in chronic heart failure has not been uniformly reported, and the use of a complete CONSORT diagram has been low, although it seems to be improving. All study participants should be accounted for within a CONSORT diagram in any RCT to enable the practicing cardiologist to interpret how the results should influence his/her clinical practice.
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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 | 0.739 | 0.879 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".