Closing the personalized medicine information gap: HER2 test documentation practice.
Bibliographic record
Abstract
BACKGROUND: Uncertainty about human epidermal growth factor receptor-2 (HER2) testing practice in Canada continues to hinder efforts to improve personalized medicine. Pathologists routinely perform HER2 assessment for all tumors > 1 cm, and pathology is reported centrally to the provincial cancer registry. OBJECTIVES: To understand patterns of HER2 test documentation for early-stage breast cancer (BC) patients in Ontario's centralized pathology reporting system. STUDY DESIGN: Retrospective cohort study of central HER2 test documentation in early-stage BC patients diagnosed in 2006-2007. METHODS: Cohort and staging information was derived from cancer registry and admissions data. Linkage across administrative databases provided data on surgical and radiologic treatment, sociodemographic factors, diagnosis setting, and comorbidities. Pathology reports from the provincial cancer registry were reviewed for HER2 testing, hormone receptor, and grade. Unadjusted and adjusted odds ratios were calculated to determine factors related to HER2 documentation. RESULTS: A HER2 test was documented for 66% of 13,396 patients. HER2 documentation was associated with stage, hormone receptor, and tumor grade documentation. Higher stage and grade at diagnosis were also associated with HER2 documentation. All models suggested variable regional documentation patterns. Documentation did not differ by sociodemographic factors, presence of comorbidities, or surgical procedure. CONCLUSIONS: Despite a universal testing policy, the rate of centralized HER2 test documentation was lower than expected and related to disease severity. Differences in regional reporting likely reflect ascertainment bias inherent to centralized pathology reporting rather than testing access. Improved HER2 reporting is encouraged for cancer registration, quality-of-care measurement, and program evaluation.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".