Forensic Pathology Workload and Complexity: Designing a Complexity System that Accurately Represents Workload
Bibliographic record
Abstract
For the most part, workload is defined for forensic pathologists in North America by the number of cases per annum, with specific recommendations set out by the National Association of Medical Examiners (NAME) to perform no more than 250 autopsies in a year. However, this definition of workload is somewhat limiting as it doesn't reflect the case to case variability that forensic pathologists encounter. The variability translates into differing amounts of time needed on the part of the pathologist to devote to each case and those differences in time can be substantial. Complexity systems exist in surgical pathology to better reflect the case-to-case variability that surgical pathologists experience. Based on these complexity systems, departments can have a more accurate representation of workload and appropriately allocate resources and plan staffing. Many different complexity systems exist, but all of them, in their own way, attempt to lessen the gap between overvaluing simple specimens and undervaluing complex specimens. No formal system for gauging complexity exists in forensic pathology. The creation of one would provide a more detailed taxonomy to be better able to define forensic pathologists' workload and compare workload between pathologists and institutions.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".