The challenge of establishing, growing and sustaining a large biobank. A personal perspective
Bibliographic record
Abstract
Laboratory medicine professionals have a unique understanding of the wealth that biological samples bring to clinical research, and of the need for quality standards for the collection, transportation, storage and analytical phases. The expertise of laboratory physicians and scientists also adds value to the interpretation and publication of the results of clinical research studies. This is an account of the evolution of over thirty five years of the Biobank/Clinical Research Clinical Trials Laboratory at one Canadian health sciences centre. The logistical, financial, and quality management challenges are presented in growing from a small-scale facility to one that now stores three million well-characterized samples from more than seventy countries, representing five continents and five major ethnic groups. This is an account of a journey, it is not intended as a guide as to how to create an ‘ideal’ biobank. Collaboration, collegiality, consistency, creativity and clinical collaborators, are the keys to progress, but there must first be a vision, one that can expand to embrace new opportunities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".