Standardisation of practice for Canadian pathologists’ assistants
Bibliographic record
Abstract
Standardisation of practice within surgical pathology begins with standardisation of procedures and processes and results in standardisation of practice by all professionals within the surgical pathology department, including pathologists’ assistants (PAs). Healthcare in today’s world is constantly evolving, and surgical pathology is no exception. Testing that was unavailable just years ago can now be considered standard practice that defines a patient’s prognosis and treatment, such is the case with breast cancer biomarkers.1 These tests sometimes emerge into practice faster than their quality control and quality assurance practices can be universally implemented, leading to issues such as those defined by the Cameron Inquiry in Canada2 and the Barne’s Report in the UK.3 These investigations and resulting recommendations call for standardisation of practice in the department directly performing these tests via reproducible, objective, strict quality assurance and control measures, and of all preanalytical and postanalytical steps involved in handling the specimens destined for testing, including standardisation of the professionals involved in these steps. Standardisation in regards to surgical pathology applies to all tasks, beginning with specimen acceptance and accessioning and continues to specimen preparation for fixation, decalcification, ancillary testing, gross descriptions and dissections including specimen sampling, and extends to successive histology techniques such as tissue processing, staining, stain interpretation and immunohistochemistry. Most of these tasks are easily standardised, such as standardisation fixation solutions, fixation times, ischaemic times, programming tissue processors and other automated and easily reproducible tasks. However, standardising a task as interpretive and unique as gross description and dissection of a complex specimen proves more of a challenge. Traditionally, these complex, difficult to standardise functions were performed by the diagnosing pathologist themselves, and their own preferences were sometimes evident in their practice. However, over the span of years, these tasks were slowly undertaken by a new profession that began to …
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How this classification was reachedexpand
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 | 0.027 | 0.511 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".