Biospecimen Data Reporting in the Research Literature
Bibliographic record
Abstract
A substantial fraction of biomedical research depends on the reliability of human biospecimens but variations in sample manipulation during collection, processing, and storage can differentially alter molecular integrity and influence interpretation of the resulting derived data. Details of biobanking processes are rarely adequately described in research publications, preventing reviewers, readers, and scientists seeking to replicate the findings, from appreciating and adequately considering preanalytical variations contributing to results. To address these shortcomings, a set of reporting guidelines, the Biospecimen Reporting for Improved Study Quality (BRISQ) criteria, were developed in 2011. In this study we evaluated the uptake and reporting of BRISQ criteria in 324 articles across four leading biomedical journals using human biospecimens and published before (161; in 2010) and after (163; in 2014) the delineation of the BRISQ guidelines. We found that even within journals recommending use of BRISQ, manuscript-level uptake. and reporting of the relevant biospecimen information is not widespread or uniform. In the future, an enhanced biospecimen reporting strategy to better serve the needs of researchers, reviewers, and journals may be considered to strengthen research reproducibility for the benefit of the research community at large.
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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 | 0.674 | 0.898 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.061 | 0.085 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".