Barriers to publishing early phase clinical trials: the oncologists’ perspective
Bibliographic record
Abstract
INTRODUCTION: Findings from early phase studies are not always placed in the public domain. This study aims to explore why many early phase clinical oncology studies are not published, as well as identify the potential barriers investigators encountered in the publication process. METHODS: Semi-structured interviews were conducted among investigators with experience in early phase clinical oncology studies. Interviews were analyzed using reflexive thematic analysis. RESULTS: Twenty-one investigators were interviewed. The majority worked in Europe (n = 13), while other investigators were based in North America (n = 4), Asia (n = 2) or Oceania (n = 2). We identified three reasons why investigators believed publishing early phase clinical trial results was important: (1) there is an ethical and moral responsibility; (2) there should be no loss of knowledge to society; and (3) there should be no waste of resources. Four main barriers in the publication process of early phase clinical trials were identified: (1) practical barriers (eg, an increased complexity of number of trials/trial sites), (2) insufficient resources (eg, money, time and human), (3) limited motivation (eg, limited intrinsic motivation of the investigator or limited prospect of return for the sponsor), and (4) inadequate collaboration (eg, different interests between industry partners and investigators). Finally, five major stakeholders were identified that can potentially contribute to improving the publication process: (1) journal editors, (2) sponsors, (3) investigators, (4) regulatory bodies, and (5) society. Investigator suggestions for improving this process, for each stakeholder, are presented. CONCLUSIONS: This study highlights the barriers experienced in publishing early phase clinical trials. Recognizing and acknowledging these barriers is crucial to devise effective strategies to improve the publishing and public sharing of early phase clinical trials.
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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 | 0.269 | 0.537 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".