Individual Investigator Profiles of Biospecimen Use in Cancer Research
Bibliographic record
Abstract
BACKGROUND: Establishing targets for case accrual is an important component of a strategic plan for a biobank. We have previously assessed overall patterns of biospecimen use in cancer research publications in selected journals. Here we extend this analysis to consider patterns of biospecimen use in relation to cancer research programs developed by individual investigators. METHODS: We selected three individual cancer research investigators whose independent research programs began circa 1986, have been characterized by extensive use of human tumor biospecimens, and have primarily involved translational research in the areas of breast, lung, and ovarian cancer. We analyzed biospecimen and data usage in their career publications categorized by numbers, type, and format, and accompanying annotating data in terms of conformance with BRISQ reporting and ethics related criteria. RESULTS: Biospecimens were used in 313/474 (66%) of publications analyzed. The average number of biospecimens used by these research programs increased six-fold from less than 1000 in 2001-2003 to greater than 6000 in 2010-2012, and the average cohort sizes per article also increased from approximately 50 to 200 cases per study over the same period in most biospecimen categories (p<0.05). The relative proportions of different formats of biospecimens used has varied significantly and continues to change with the emergence of digital biospecimen derived data. In these three translational research programs, BRISQ elements relating to 'Biobank' categories were significantly less well reported for biospecimens used in publications than data corresponding to 'Clinical chart' categories (p<0001). CONCLUSIONS: This study shows that overall use of biospecimens in cancer research has increased significantly and that dynamic variation in the relative use of different biospecimen formats has also occurred. This study also confirms our previous findings on patterns of biospecimen use and also those concerning incomplete reporting of relevant data elements that has not improved in the past decade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".