Improving the Quality of Abstract Reporting for Phase I Cancer Trials
Bibliographic record
Abstract
PURPOSE: Conference abstracts of phase I trials (P1T) communicate important anticancer drug development information. Our objectives were to determine elements essential for good P1T abstract reporting, to assess the quality of P1T abstracts submitted to American Society of Clinical Oncology (ASCO) meetings, and to propose reporting guidelines. EXPERIMENTAL DESIGN: A survey of developmental therapeutics experts established elements of P1T reporting quality, and a scoring system was generated. All P1T abstracts published in ASCO Annual Proceedings from 1997 to 2006 were reviewed, and the scoring system was applied. RESULTS: A survey was distributed twice to 69 experts, with a response rate of 39% (27 of 69). Experts rated 37 elements using a five-point scale, and elements with mean ratings over 3.75 were included in the final scoring system. One thousand six hundred and eighty three P1T abstracts were reviewed. A positive and linear association was observed between average expert rating of the elements and the proportion of P1T abstracts including those elements (Spearman correlation coefficient, rho = 0.60, P < 0.001). The median for all 1,683 abstracts was 62.5% (range, 25-95%; SD, 12.3%). Year of presentation was found to be significantly associated with higher quality scores (rho = 0.20, P < 0.001), with later years possessing better quality scores. The quality score was statistically significant as a predictor of type of presentation (odds ratio, 1.10; 95% confidence interval, 1.02-1.19 per 10% increase; P = 0.014), with oral presentations having the highest scores. CONCLUSIONS: The quality of P1T abstract reporting at ASCO has improved over time, although there is room for optimization. The quality of P1T abstract reporting may be enhanced using guidelines derived from our expert consensus.
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 | 0.595 | 0.832 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".