Reporting quality of CONSORT flow diagrams in published early phase dose-finding clinical trial reports: Improvement is needed
Bibliographic record
Abstract
BACKGROUND: This project aims to: (1) assess the completeness of information in flow diagrams of published early phase dose-finding (EPDF) trials based on CONSORT recommendations, and if additional features on dose (de-)escalation were presented; (2) propose new flow diagrams presenting how doses were (de-)escalated throughout the trial. METHODS: Flow diagrams were extracted from a random sample of 259 EPDF trials, published from 2011 to 2020 indexed in PubMed. Diagrams were scored out of 15 following CONSORT recommendations with an additional score for presence of (de-)escalation. New templates were proposed for features that were deficient and presented to 39 methodologists and 11 clinical trialists in October and December 2022. RESULTS: 98 (38%) papers included a flow diagram. Flow diagrams were most deficient in the reporting of reasons for lost to follow up (2%) and reasons for not receiving allocated intervention (14%). Few (39%) presented sequential dose-decision stages. Of voting methodologists, 33/38 (87%) agreed or strongly agreed that for participants recruited in cohorts, presenting the (de-)escalation steps in the flow diagram is a useful feature, also expressed by the trial investigators. Most workshop attendees (35/39, 90%) preferred a larger dose to be displayed higher up within the flow diagram than a smaller dose. CONCLUSION: Most published trials do not provide a flow diagram, and for those that do, essential information is often omitted. EPDF flow diagrams capturing information on participant flow in the trial's journey, encapsulated within one figure, are highly recommended to promote transparency and interpretability of trial results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.922 | 0.941 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.026 | 0.014 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads 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".