Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data
Bibliographic record
Abstract
IntroductionRecurrence free survival is frequently investigated in cancer outcome studies, however is not explicitly documented in cancer registry data that is widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, chemotherapy or radiation may herald recurrence. Objectives and ApproachThis study aimed to develop and validate algorithms for identifying breast cancer recurrence using large administrative data.Two cohorts with high recurrence rates were used: 1) all young (≤ 40 years) breast cancer patients (2007-2010), and 2) all neoadjuvant chemotherapy patients (2012-2014) in Alberta, Canada. Health events after primary treatment were obtained from the Alberta cancer registry, physician billing claims, and vital statistics databases. Positive recurrence status (defined as either locoregional, distant or both) was ascertained by primary chart review. The cohort was divided into a developing (60%) and validating (40%) set. Development of algorithms geared towards high sensitivity, PPV and accuracy respectively were performed using classification and regression tree (CART) models. Key variables in the models included: a new round of chemotherapy, a second mastectomy, and a new cluster of radiologist, oncologist or general surgeon visits occurring after the primary treatment. Compared with chart review data, the sensitivity, specificity, PPV, NPV and accuracy of the algorithms were calculated. ResultsOf 606 patients, 121 (20%) had recurrence after a median follow-up 4 years. The high sensitivity algorithm had 94.2% (95% CI: 90.1-98.4%) sensitivity, 92.8% (90.5-95.1%) specificity, 76.5% (70.0-88.3%) PPV, 98.5% (97.3-99.6%) NPV and 93.1% (91.0-95.1%) accuracy. The high PPV algorithm had 74.4% (66.6-82.2%) sensitivity, 97.8% (96.5-99.2%) specificity, 90.0% (84.1-95.9%) PPV, 93.6% (91.4-95.7%) NPV and 92.9% (90.9-95.0%) accuracy. The high accuracy algorithm had 88.4% (82.7-94.1%) sensitivity, 97.1% (95.6-98.6%) specificity, 88.4% (82.7-94.1%) PPV, 97.1% (95.6-98.6%) NPV and 95.4% (93.7-97.1%) accuracy. Conclusion/ImplicationsThe proposed algorithms achieved favourably high validity for identifying recurrence using widely available administrative data. Further study may be needed for improving sensitivity and PPV, and validating the algorithms in larger data for widespread use.
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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 | 0.028 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".