The quality of phase I trial (P1T) abstracts submitted to ASCO meetings
Bibliographic record
Abstract
6532 Background: Conference abstracts of P1T communicate important information of anticancer drug development. Our objectives were to determine elements considered by experts as essential for good P1T abstract reporting, to assess the quality of P1T abstracts submitted to ASCO meetings, and to propose guidelines for future reporting. Methods: Elements important for P1T abstract reporting were determined by a survey of experts in developmental therapeutics, and a scoring system for abstract quality was generated. All P1T abstracts published in ASCO Proceedings from 2002–2006 were reviewed, and a quality score was assigned. Results: An electronic survey was sent twice to 69 experts, with a response rate of 39% (27/69). Characteristics of the 27 experts were: average age = 48; male = 74%, USA:Europe:Canada = 78%:15%:7%; 89% had 10+ years experience in drug development; 93% from academic institutions versus 7% from governmental agencies; 56% currently involved in clinical research versus 44% in translational research. Experts were asked to rate each of 37 elements using a five-point Like rt scale, and elements with average expert ratings over 3.75 were included in the final quality score calculations. A total of 920 P1T abstracts over 5 years were reviewed. A positive and linear association was observed between average expert rating of the elements and proportion of P1T abstracts that included those elements (Spearman correlation coefficient, ρ=0.65). The median quality score for all 920 abstracts was 65% (range 26%–95%, SD 12.6%). Deficiencies existed in abstract reporting; for instance, dose-limiting toxicity was described in only 63% of abstracts, while recommended dose or maximum tolerated dose was reported in only 38%. A significant association between year of presentation was found (ρ=0.36, P<0.001), with later years possessing better quality scores. The quality score was also statistically significant as a predictor of type of presentation (odds ratio 0.20, 95% CI 0.08–0.54, P=0.002), with oral presentations having the highest scores. Conclusion: 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. No significant financial relationships to disclose.
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 | 0.166 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".