Quality of Abstracts Describing Randomized Trials in the Proceedings of American Society of Clinical Oncology Meetings: Guidelines for Improved Reporting
Bibliographic record
Abstract
PURPOSE: To evaluate the quality of reporting in abstracts describing randomized controlled trials (RCTs) included in the Proceedings of American Society of Clinical Oncology (ASCO) meetings and to propose reporting guidelines for abstracts that are submitted to future meetings. METHODS: Guidelines for reporting of RCTs in abstracts were developed by extracting key elements from published guidelines for full reports of RCTs, and modified based on an expert survey. Abstracts presenting results of RCTs with sample size > or = 200 were identified from the ASCO Proceedings for the years 1989 to 1998. Information regarding the quality of each abstract was extracted, and a quality score (possible range, 0 to 10) was assigned based on adherence to the guidelines. RESULTS: Brief description of the intervention, explicit identification of the primary end point, and presentation of results accompanied by statistical tests were regarded by experts as the most important items to include in an abstract, whereas presentation of secondary and subgroup analyses was the least important. Deficiencies in reporting were present in almost all of the 510 abstracts; for example, only 22% of the abstracts provided explicit identification of the primary end point. The median quality score was 5.5 (range, 2.0 to 8.5); the quality score improved with time (P <.0001) and was better for oral or plenary presentations (P =.0003). CONCLUSION: The quality of reporting of RCTs in abstracts submitted to Annual Meetings of ASCO is suboptimal. Although space precludes the inclusion of details required in the final report, abstracts could be improved through the use of explicit minimal guidelines, which are suggested in this article.
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 | 0.832 | 0.927 |
| Meta-epidemiology (narrow) | 0.008 | 0.009 |
| Meta-epidemiology (broad) | 0.022 | 0.025 |
| Bibliometrics | 0.053 | 0.053 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".