What Is the Best Indicator to Determine Anatomic Pathology Workload?
Why this work is in the frame
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Bibliographic record
Abstract
Health care resources in Canada are limited, and there is tremendous pressure to reduce laboratory budgets. It is almost impossible to acquire new pathologist positions to adequately meet service demands. There is a need to determine objectively the appropriate pathologist workload. I looked at multiple indicators (total accessioned cases, number of specimens, slides, blocks, Royal College of Pathologists [London, England] model, population served, and level 4 equivalent [L4E]) that might reflect the pathologist's work and determine which indicators are the best. L4E is a calculated weighted value based on complexity levels of individual anatomic pathology consultation. Of all indicators analyzed, L4E is the best reflection of a pathologist's anatomic pathology work because it has the best statistical values, is a direct output measurement of pathologist consultations, and uses data routinely collected in many North American laboratories.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it