Development of the CCRU kit squad: Centralization of biospecimen “kit” management.
Bibliographic record
Abstract
295 Background: Personalized medicine has resulted in a rapid increase in biospecimens collection. Each biospecimen collected requires supplies that are provided in the form of “kits”. The number of kit types per protocol ranges anywhere from 2-60, with an average of 30 different kit types per trial. Historically, each trials nurse has managed their own kits resulting in large amounts of nursing time being spent on kit management. Kits took up a large amount of space in clinical areas, including expired kits, and they were being managed in no standardized fashion. Due to rapidly increasing biospecimen volumes, existing methods of kit management were no longer feasible and the CCRU Kit Squad was developed. Methods: Over the course of 12-mos, an extensive assessment of current kit management practices were reviewed with all disease site groups, including workflows and quantities utilized. An e-commerce software platform was selected, and semi-customized to centralize online ordering and receiving of kits, and the creation of a central location was setup for kit storage and daily operations. On-boarding of each group included retrieving existing kits from each nurse, uploading kits to the software, training each nurse to use the software, setting up accounts with each respective vendor for deliveries and re-supply, and disposing of expired kits. Results: Disease site groups were transitioned to the CCRU Kit Squad stepwise from Oct 2016 to Dec 2017 (15-mos), which included 80 nurses, 16 disease site groups, and over 400 clinical trials. By the end of 2017, the average number of kits ordered per day, and per month were 33 and 1003, respectively. Conclusions: The CCRU Kit Squad developed a centralized online service for kit management, thereby reducing administrative burden on clinical trial nurses. It has streamlined the management of biospecimen kits, eliminated wasted space in clinical areas, and facilitated the selection of the right kit for the right test at the right time.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".