Factors Associated with Publication of Randomized Phase iii Cancer Trials in Journals with a High Impact Factor
Bibliographic record
Abstract
BACKGROUND: Impact factor (if) is often used as a measure of journal quality. The purpose of the present study was to determine whether trials with positive outcomes are more likely to be published in journals with higher ifs. METHODS: We reviewed 476 randomized phase iii cancer trials published in 13 journals between 1995 and 2005. Multivariate logistic regression models were used to investigate predictors of publication in journals with high ifs (compared with low and medium ifs). RESULTS: A positive outcome had the strongest association with publication in high-if journals [odds ratio (or): 4.13; 95% confidence interval (ci): 2.67 to 6.37; p < 0.001]. Other associated factors were a larger sample size (or: 1.06; 95% ci: 1.02 to 1.10; p = 0.001), intention-to-treat analysis (or: 2.53; 95% ci: 1.56 to 4.10; p < 0.001), North American authors (or for European authors: 0.36; 95% ci: 0.23 to 0.58; or for international authors: 0.41; 95% ci: 0.20 to 0.82; p < 0.001), adjuvant therapy trial (or: 2.58; 95% ci: 1.61 to 4.15; p < 0.001), shorter time to publication (or: 0.84; 95% ci: 0.77 to 0.92; p < 0.001), uncommon tumour type (or: 1.39; 95% ci: 0.90 to 2.13; p = 0.012), and hematologic malignancy (or: 3.15; 95% ci: 1.41 to 7.03; p = 0.012). CONCLUSIONS: Cancer trials with positive outcomes are more likely to be published in journals with high ifs. Readers of medical literature should be aware of this "impact factor bias," and investigators should be encouraged to submit reports of trials of high methodologic quality to journals with high ifs regardless of study outcomes.
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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 | 0.072 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".