Custom R Flexdashboard for molecular genetic pathology quality tracking
Bibliographic record
Abstract
The practice of modern-day laboratory medicine entails extensive, daily practice of tracking various quality metrics of every molecular test to ensure quality maintenance, as well as for laboratory management. While various third-party tools are commercially available, they represent a significant investment for publicly funded institutions. To automate aspects of this quality management, we developed a custom dashboard, written using R. We used R Studio, a freely available software, and employed the Shiny and Flexdashboard packages to develop the code base for the dashboard. Data for the dashboard were pulled from multiple Excel tracking spreadsheets for different clinical assays. The current dashboard allows for dynamic, automated reporting of case volume, and turn-around time, which are regularly reported metrics to CancerCare Ontario for reimbursement purposes. Workload tracking is also made possible, automating calculations regularly performed for billing purposes. The dashboard summarizes various quality metrics for each assay in a single table, viewable by multiple personnel within a single network. Additional features such as filtering quality metrics by date and customization of a variety of plots were also included. Whereas other informatics solutions may be available, our custom solution represents a low-cost system that alleviates a significant workload from various members of the laboratory medicine department, easing the currently significant administrative burden from the “hands-on” staff. Future work will be focused on further improving the accessibility of the dashboard and the integration of additional molecular assays for quality monitoring.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.050 |
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".