Presentation of Nonfinal Results of Randomized Controlled Trials at Major Oncology Meetings
Bibliographic record
Abstract
PURPOSE: To assess the frequency, implications, and factors associated with reporting nonfinal analyses (NFAs) of randomized controlled trials (RCTs) as abstract publications. METHODS: We identified 138 consecutive reports of RCTs testing systemic therapy for lymphoma, breast, colorectal, or non-small-cell lung cancer published in six major journals between 2000 and 2004. We then searched proceedings of seven major cancer meetings, 1990 to 2004, for abstracts related to these publications which presented efficacy results. Articles and abstracts were compared for discordance in sample size, median follow-up, results, and conclusions. Abstracts were evaluated for statements explicitly noting or implying that results were not final. Factors associated with discordance were assessed by uni- and multivariate analyses. RESULTS: We identified 303 related abstracts; 197 were eligible. In 86 abstracts (44%), results were stated or implied to be NFA; this was explicitly stated in 41 (21%). The NFAs included 12 where accrual was ongoing. Discordance with article was found in 124 abstracts (63%) and was more common with NFAs (67 of 86 [78%] v 57 of 111 [51%]; P = .0001). When compared with articles, authors' conclusions were substantively different in 17 abstracts (10%). Factors most associated with data discordance were lymphoma trial (odds ratio [OR], 3.8; 95% CI, 1.5 to 10.8), cooperative group trial (OR, 2.8; 95% CI, 1.4 to 5.6), and presentation of a NFA (OR, 2.9; 95% CI, 1.5 to 5.8). CONCLUSION: Meeting abstracts often include NFAs and are frequently discordant with subsequent article publication.
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 | 0.671 | 0.928 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.036 | 0.022 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| 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".