Automated Cell Isolation Laboratory Information System
Bibliographic record
Abstract
Effective data collection of donor and islet isolation records is an essential part of any clinical islet transplant program. We have recently developed a customized human islet laboratory information system designed to meet the record keeping, safety standards, and research needs of our human islet laboratory. The system supports data captured in all phases of islet isolation, from the moment the organ is offered to the program to when the last microbiology report is entered into the database. It uses applications that run on desktop computer over standard networks and handheld computer over wireless connection. The system uses Compaq iPAQ™ handheld computers running over wireless network and as an “electronic clipboard,” permitting direct data entry into the database during the islet isolation process. This system has been designed and successfully implemented by the Edmonton Clinical Islet Isolation Laboratory for the classification of information in such areas as materials management (from barcode-based management of media to vendor information) and isolation data capture (including donor and procurement information, microscope image capture of islets during different phases, and determination of yield). The system was intended to meet both Health Canada and Food and Drug Administration (FDA) requirements with respect to user ID and full auditable history of all records, and to be considered equivalent to hard-copy records. Designed as a multi-user system, it supports team collaboration in building a unified, comprehensive digital record of the isolation for comparative purposes. It promotes the capture of isolation data in a consistent, legible, and structured fashion, making it readily usable for comparison, queries, and analysis, as well as making it an excellent tool for training and research in the field of human cell isolation and clinical cell transplantation data records.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.049 |
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".