Improving consistency of high quality diagnosis, staging, and treatment using measurement.
Bibliographic record
Abstract
217 Background: To improve variability in diagnosing and treating cancer resection cases, six Canadian provinces implemented standardized pathology checklists to transition from narrative to synoptic reporting. In clinical practice, pathologists are electronically capturing data on the resected cancer specimens synoptically for breast, colorectal, lung, prostate, and endometrial cases. Though data were collected in a standardized format, consensus based indicators were unavailable to coordinate action across Canada. Objectives: We aimed to develop indicators to measure consistency of high quality cancer diagnosis, staging, prognosis and treatment, and coordinate action. Methods: A literature review was conducted with the input of clinical experts to inform the development of indicators. 50 clinicians from x jurisdictions reviewed, selected and ranked 33 indicators, initially drafted. Clinicians also provided input on the clinical validity of the indicators and set targets based on evidence. Clinicians reviewed the baseline data, confirmed the clinical usefulness of indicators, and assigned indicators into three pioneered domains. Results: 47 indicators were developed and categorized into one of three domains: descriptive, which provide data on intrinsic measures of a patient’s tumour, such as stage or tumour type; process, which measure the quality of data completeness, timeliness and compliance; and clinico-pathologic outcome, which examine surgeon or pathologist effect on the diagnostic pathway, such as margin positivity rates or adequacy of lymph node removal. Examples of indicators are: margin status; lymph node examined, involved and retrieval; histologic type and grade distribution; lympho-vascular invasion; pT3 margin positivity rate. Conclusions: The indicators have set a framework for: measuring consistency and inconsistency in diagnosing and staging cancer; for organizing conversations and multidisciplinary group discussions; and establishing the culture of quality improvement.
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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 | 0.236 | 0.342 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".