Reqscan: An open source solution for laboratory requisition scanning, archiving and retrieval
Bibliographic record
Abstract
Requisition storage and retrieval are an integral part of the outpatient laboratory testing process. It is frequently necessary to review an original requisition to confirm the ordering physician, patient demographics, diagnostic information, and requested tests. Manual retrieval of a paper requisition is time-consuming and tedious. Although commercial solutions exist for the scanning and archiving of barcoded paper requisitions, the tools to accomplish this are freely available from the open source software community. We present a simple dedicated piece of software, Reqscan, for scanning patient laboratory requisitions, finding all barcode information, and saving the requisition as a portable document format named according the barcode(s) found. This Python application offers a simple solution to patient requisition digitization. Reqscan has been successfully tested and implemented into routine practice for storage and retrieval of outpatient requisitions at St. Paul's Hospital, Department of Pathology and Laboratory Medicine in Vancouver, British Columbia, Canada.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.065 |
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".