Analysis of Trends in Biospecimen Complexity in Cancer Research Over Two Decades
Bibliographic record
Abstract
Background: Over time, researchers' demand for increased quality and quantity of biospecimens has risen. However, quality is multifaceted, ranging from simple to complex, and comes at a cost. Therefore, to be sustainable and ensure optimal utilization of their resources (supply), biobanks must consider the trends in biospecimen use to predict the needs for future biospecimen quality (demand). Methods: An unbiased selection process was used to identify research articles from across the spectrum of cancer research from the PubMed database. A set of 225 articles utilizing human biospecimens were randomly selected for review (75 articles from each of three time intervals; 2000, 2010, 2020). Criteria for determining the source and complexity of quality of biospecimens were developed and overall concordance between two independent observers abstracting the data was then confirmed (k = 0.87) to validate the criteria. Results: We observed increased use of dual biospecimen formats (20%–36% of articles, p = 0.03), matched samples (16%–37% of articles, p = 0.0033), and biospecimens with associated outcomes data (20%–49%, p = 0.0002). In addition, the use of two or more cohorts increased over time (p = 0.03). The mechanism through which biospecimens were obtained also changed over time with an increase in the diversity of collection pathways used (p = 0.006). Conclusions: The complexity of biospecimens being used in cancer research and the diversity of collection pathways through which these are obtained has changed significantly. This observation is important for biobanks given that the cost to support the supply of biospecimens with complex extrinsic as opposed to simple intrinsic quality characteristics is greater. For biobanks to manage sustainability, optimize utilization, and meet changing research demand, they may need to adjust their operational models to better support the supply of these types of biospecimens.
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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 | 0.064 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.036 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".