Pathology reporting to cancer registries: The evaluation of an electronic reporting system
Bibliographic record
Abstract
Anonymized abstracted information from all pathology reports from January 1996 to March 2005 was obtained from the OCR. 35 sites were analyzed for the timeliness and completeness of pathology reporting. The study objective is to measure the distribution of timeliness and completeness of pathology reporting from centers across Ontario to the Ontario Cancer Registry (OCR), to determine how these distributions change over time, especially with the implementation of electronic reporting (E-Path) and to evaluate the timeliness and completeness of reporting against established quality targets. E-Path implementation was associated with improved timeliness and completeness. E-Path reports were received 3 times faster, demographic information was more complete and reporting volume increased by approximately 19%. Addressing the underlying causes for this unpredictability could lead to further quality improvement. Modeling of report timeliness and completeness demonstrated that E-Path implementation could be associated with temporary abnormal patterns in reporting and, while improved, the pattern of reporting remained unpredictable in several sites.
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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 | 0.160 | 0.313 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".