Publication bias is present in blood and marrow transplantation: an analysis of abstracts at an international meeting
Bibliographic record
Abstract
Publication bias is the preferential publication of research with positive results, and is a threat to the validity of medical literature. Preliminary evidence suggests that research in blood and marrow transplantation (BMT) lacks publication bias. We evaluated publication bias at an international conference, the 2006 Center for International Blood and Marrow Transplant Research (CIBMTR)/American Society for Blood and Marrow Transplantation (ASBMT) "tandem" meeting. All abstracts were categorized by type of research, funding status, number of centers, sample size, and direction of the results. Publication status was then determined for the abstracts by searching PubMed. Of 501 abstracts, 217 (43%) were later published as complete manuscripts. Abstracts with positive results were more likely to be published than those with negative or unstated results (P = .001). Furthermore, positive studies were published in journals with a mean impact factor of 6.92, whereas journals in which negative/unstated studies were published had an impact factor of only 4.30 (P = .02). We conclude that publication bias exists in the BMT literature. Full publication of research, regardless of direction of results, should be encouraged and the BMT community should be aware of the existence of publication bias.
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 | 0.241 | 0.514 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.031 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".