Publication Bias in Blood and Marrow Transplantation
Bibliographic record
Abstract
Only a small proportion of abstracts lead to full publication. Abstracts with "positive" results are more likely to be published than other abstracts, leading to publication bias. To date, this issue has not been examined in the blood and marrow transplantation (BMT) literature. We hypothesized that because BMT centers are often based at academic centers, the proportion of abstracts leading to publication will be high. All abstracts presented at the Canadian Blood and Marrow Transplant Group biannual meetings in 2002, 2004, and 2006 were reviewed and categorized by study type, funding source, single-center or multicenter study, form of presentation, and positive or negative results, using the authors' definitions. To determine publication, each reference was searched on multiple databases (MEDLINE, EMBASE, Web of Science, and CINAHL) by first, second, and final author names. Two authors performed abstract categorization and searching, and disagreements were resolved by consensus. Of the 141 abstracts reviewed, only 43 were published (30.4%). Twenty-one studies were published from 2002 (36.8%), compared with 12 from 2004 (24.0%) and 10 from 2006 (29.4%) (P = .35). Neither positive results nor the number of involved centers were associated with the likelihood of publication. Clinical studies (retrospective or prospective) were more likely to be published than nonclinical studies (P = .014). Funded studies and oral presentations were more likely to be published (P = .009 and .004, respectively). A low rate of publication is seen in the field of BMT. Studies with clinical outcomes, externally funded studies, and studies presented orally were more likely to be published. However, there was no publication bias in favor of studies with positive results. Publication bias should be evaluated further at larger BMT meetings, and efforts should be made to encourage full publication of scientific abstracts.
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 | 0.104 | 0.310 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".