Workload Measurement in Subspecialty Placental Pathology in Canada
Bibliographic record
Abstract
BACKGROUND: Workload measurement is important to help determine optimal staffing and workload distribution for pathology laboratories. The Level 4 Equivalent (L4E) System is the most widely used Anatomical Pathology (AP) workload measurement tool in Canada. However, it was initially not developed with subspecialties in mind. METHODS: In 2016, a Pan-Canadian Pediatric-Perinatal Pathology Workload Committee (PCPPPWC) was organized to adapt the L4E System to assess Pediatric-Perinatal Pathology workload. Four working groups were formed. The Placental Pathology Working Group was tasked to develop a scheme for fair valuation of placental specimens signed out by subspecialists in the context of the L4E System. Previous experience, informal time and motion studies, a survey of Canadian Pediatric-Perinatal Pathologists, and interviews of Pathologists' Assistants (PA) informed the development of such scheme. RESULTS: A workload measurement scheme with average L4E workload values for examination and reporting of singleton and multiple gestation placentas was proposed. The proposal was approved by the Canadian Association of Pathologist - Association canadienne des pathologistes Workload and Human Resources Committee for adoption into the L4E System. CONCLUSION: The development of a workload measurement model for placental specimens provides an average and fair valuation of these specimen types, enabling its use for resource planning and workload distribution.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".