Implementing Epic Beaker Laboratory Information System for Diagnostics in Anatomic Pathology
Bibliographic record
Abstract
Consolato M Sergi1,2 1Anatomic Pathology Division, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada; 2Deparment of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, CanadaCorrespondence: Consolato M Sergi, Tel +1 613-737-7600 x 2427, Fax +1 613-738-4837, Email csergi@cheo.on.caAbstract: Medicine is expeditiously evolving, and the number of diagnostic opportunities has increased exponentially in the last decade. Electronic medical records (EMRs) have been welcomed in most institutions worldwide following an early period of suspicious behavior. Unfortunately, several cracks dictated the initial approach to hospital systems and leadership incompetency. However, the pathway for a successful decade of EMRs is paved. This narrative review illustrates some principles implementing Epic Beaker software for anatomic pathology in academic medical institutions. Implementing such software improves the diagnostic approach in the division of anatomic pathology because the pathologists can directly access an enormous amount of clinical and radiological information now at their front desk using extremely versatile windows.Keywords: diagnostics, laboratory information system, Epic, Beaker, quality assurance
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".