Using standardized electronic pathology data to identify practice variation, potential impact to patient outcomes, and quality improvement initiatives.
Bibliographic record
Abstract
45 Background: Practice variation in diagnosis and treatment exists between clinicians and regions across Canada. This variation can impact the quality of care that patients receive and affect patient outcomes. We aimed to gain a better understanding of the scale and type of variation between clinicians and provinces within the cancer system. Methods: Fifty pathologists, surgeons, and medical oncologists from 10 regions were convened to leverage literature and the College of American Pathologists data standards to create 48 indicators related to five cancers: breast, lung, colorectal, endometrial and prostate. Six months of synoptic pathology data were used to generate the indicators, which were reviewed by 65 clinicians to identify practice variation and potential quality improvement areas. Results: Five provinces generated 48 indicator data analyses. Practice and performance variation across five cancer sites and jurisdictions was found. For example, guidelines recommend examining at least 12 lymph nodes in colorectal cancer resections as this directly impacts staging, treatment and patient prognosis. Only one province met this guideline in 90% of cases. Another example is Lynch syndrome testing, a hereditary condition that increases the risk of developing multiple primary cancers – particularly colorectal and endometrial. The data showed unequitable access to screening with 0-70% of colorectal cancer patients aged ≤70 years and only 10-40% of endometrial cancer cases being screened for Lynch syndrome. The value of these indicators is enormous to inform potential training opportunities and set standards of care at the local or broader clinical governance level so that consistent, high-quality care is delivered in accordance with evidence-based guidelines. Conclusions: Practice variation exists between clinicians and jurisdictions. In two jurisdictions (200 pathologists), comparative pathology indicator data are being used to self-reflect and converse with peers with the goal of reducing practice variation and improving patient care.
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 | 0.055 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".