Factors Influencing the Publication of Randomized Controlled Trials in Child Health Research
Bibliographic record
Abstract
BACKGROUND: Publication bias threatens the validity of clinical decisions. The root causes are relatively unknown, and there is limited investigation in child research literature. OBJECTIVES: To identify factors associated with subsequent nonpublication of abstracts presented at the Society for Pediatric Research meetings, and to determine the relative importance of the reasons identified for nonpublication. DESIGN: A cross-sectional survey was used to ask researchers about their reasons for the selective publication of randomized controlled trials (RCTs). The authors of 393 RCTs presented at the Society for Pediatric Research meetings from 1992 to 1995 were surveyed. A modified Total Design Method for mail surveys was used, with a reminder sent to all potential respondents 1 week after the initial mailing and full mailings sent to nonrespondents at 3 and 10 weeks following the initial mailing. RESULTS: One hundred sixty-six (45%) completed surveys were returned, and 119 (72%) abstracts were published as full manuscripts. Factors significantly associated with nonpublication identified through multiple logistic regression were the respondent's report of scientific merit and significance of results. Of the 47 studies that were not published, only 8 (17%) had been submitted for publication. Authors of unpublished studies identified the following as important reasons for not publishing: not enough time (56.4 responded important or very important); trouble with coauthors (28.9); and journal unlikely to accept (26.3). CONCLUSIONS: Of the RCTs presented and not subsequently published, the majority (83%) were never submitted for publication. The most common reason cited by authors for nonpublication was lack of time.
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 | 0.746 | 0.930 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| 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; 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".