Neglecting the null: the pitfalls of underreporting negative results in preclinical research
Bibliographic record
Abstract
ABSTRACTHeightened competition for funding and increased pressure to publish in high-impact journals has led to a modern-day publication culture that favours positive results. The underreporting of negative, or null, results is a form of publication bias that occurs when researchers and/or reviewers fail to communicate findings due to unfavourable directionality or perceived unimportance. For nearly three decades, recognition of this bias in clinical research has led to revised policies and guidelines in an effort to improve reporting transparency and accuracy. Only recently has the existence of this reporting bias been fully appreciated as a formidable problem in preclinical research. Considering that preclinical research provides the foundation on which many clinical trials are conceived, finding solutions to increase the reporting accuracy of preclinical studies is of paramount importance. In this commentary, we will explore how the underreporting of negative results in preclinical research distorts scientific knowledge and subsequently misguides clinical research. We will conclude with several suggestions for reducing this bias with the intention of transitioning towards a truly transparent and objective publishing landscape.
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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 | 0.661 | 0.831 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".