CAP-ACP Workload Model for Advanced Diagnostics in Precision Medicine
Bibliographic record
Abstract
OBJECTIVES: In precision medicine, where oncologic management is tailored to the individual's clinical and genetic profiles, advanced diagnostic testing provides prognostic information and guides management in a growing number of malignancies. There is a need to capture the work pathologists perform to meet this demand by providing medically relevant, timely, and accurate testing results. This work includes not only direct patient consults (interpretation of results and issuing reports) but the administrative and medical oversight as well as the research needed to provide the necessary quality assurance, quality control, direction, and framework for the laboratory. METHODS: An expert panel of Canadian pathologists involved in advanced diagnostics was convened to establish and beta test a model for workload assessment in advanced diagnostics. RESULTS: All aspects of the advanced diagnostics workload were detailed and applied to models based on members' experience, including medical oversight, administration, and the introduction of new testing and platforms. Models for biomarker testing were developed for simple and complex or multiplexed assays, and a detailed model was developed to assess the workload for next-generation sequencing-based assays. CONCLUSIONS: This paper provides the first detailed proposal for capturing an advanced diagnostic workload to enable appropriate pathologist allotment for performing all the steps required to run an advanced diagnostic service.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".