Workload measurement in subspecialty dermatopathology
Bibliographic record
Abstract
AIM: To measure pathologist workload in subspecialty dermatopathology. METHODS: Three subspecialty dermatopathologists, working in a university-affiliated laboratory, participated in a time-motion study during which they reported 2891 consecutive skin cases received from community-based dermatologists. All pathology reports were retrospectively reviewed and workload measured using the Royal College of Pathologists (RCPath) guidelines and the level 4 equivalent (L4E) method. RESULTS: The majority of dermatopathology cases were scored as low (32%) or intermediate (52%) complexity using the RCPath matrix. Only 16% of cases were considered high or very high complexity. The mean RCPath score per case was 2.68 units. Using L4E complexity levels, 83% of specimens were level 3, 15% were level 4, and only 2% were higher complexity (levels 5 and 6). Mean values for specimens/case, blocks/case, and slides/case were 1.31, 1.52, and 2.92, respectively. Time-motion analysis demonstrated a mean workload per hour of 16.3 cases, 21.3 specimens, 45.1 slides, 43.0 RCPath units, and 12.2 L4E. All three dermatopathologists reported >35 RCPath units per hour. CONCLUSIONS: The RCPath histopathology workload guidelines underestimate the workload achievable by an experienced dermatopathologist, and thus are not directly applicable to subspecialty dermatopathology practice. Hourly work rates 3-4 times that recommended by the RCPath workload matrix are routinely achievable, but extrapolation to yearly workload estimates requires detailed knowledge of practice pattern and time required for non-clinical duties such as teaching, research and administration.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".