An Online Tool for Improving Biospecimen Data Element Reporting
Bibliographic record
Abstract
Detailed documentation of the experimental materials and methods is essential for the validation of scientific papers. Human biospecimens are increasingly utilized as materials in cancer research and information about the biospecimens used is a component of this documentation. We hypothesized that previously reported biospecimen data are inadequate for accurate replication and/or validation of a substantial proportion of studies. To examine this issue, we analyzed biospecimen reporting in a representative cross section of publications over the past 12 years (1998, 2004, 2010) in the journals, Cancer Research (CR, n=46) and Clinical Cancer Research (CCR, n=73). We assessed biospecimen data in relation to the standards outlined as the Tier 1 recommended data elements from the Biospecimen Reporting for Improved Study Quality (BRISQ), in addition to ethics criteria. These data elements encompass features of biospecimens influenced by the patient, medical procedure, and biospecimen acquisition, handling and storage processes. Analysis found that while there was a significant increase in the reporting of ethics board approval status (p<0.008) and name of the ethics board (p<0.0001), there were no significant differences between these journals or over this period in reporting other biospecimen-related data elements. Of the 15 Tier 1 data elements assessed in CR and CCR, the data elements commonly obtained from the "Clinical Chart" (8/15 elements) were significantly better reported than elements that would typically be obtained from the "Biobank" (p<0.0001). Our findings demonstrate that reporting of biospecimen-related data elements has been incomplete. As one part of the solution to this issue, we propose the use of an online data-elements reporting tool (www.biobanking.ca) by biobanks. This BRISQ Report tool aims to help biobanks provide the relevant biospecimen-related data as a structured report, and to promote its inclusion as supplementary material in publications to improve the quality of future research studies.
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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 | 0.180 | 0.451 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.043 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.131 | 0.050 |
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".