The Availability of Human Biospecimens to Support Biomarker Research
Bibliographic record
Abstract
Preserved biospecimens held in biobank inventories and clinical archives are important resources for biomarker research. Recent advances in technologies have led to an increase in use of clinical archives in particular, in order to study retrospective cohorts and to generate data relevant to tissue biomarkers. This raises the question of whether the current sizes of biobank inventories are appropriate to meet the demands of biomarker research. This commentary discusses this question by considering data concerning overall biobank and biospecimen numbers to estimate current biospecimen supply and use. The data suggests that biospecimen supply exceeds current demand. Therefore, it may be important for individual biobanks to reassess the targets for their inventories, consider culling unused portions of these inventories, and shift resources towards providing prospective custom biobanking services.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".