Developing case‐finding algorithms for second events of oropharyngeal cancer using administrative data: A population‐based validation study
Bibliographic record
Abstract
BACKGROUND: Second event (recurrence or second primary cancer)-free survival is an important indicator for assessing treatment efficacy. However, second events are not explicitly documented in administrative data such as cancer registries. Thus, validated algorithms using administrative data are needed to identify second events of oropharyngeal cancers. METHODS: The algorithms were developed using classification and regression tree models. Data from chart review served as the reference standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. RESULTS: The high-sensitivity algorithm achieved 87.9% (95% confidence interval: 82.2%-93.6%) sensitivity, 84.5% (81.1%-87.8%) specificity, 61.2% (54.1%-68.4%) PPV, 96.2% (94.2%-98.1%) NPV, and 85.2% (82.3%-88.1%) accuracy. The high-PPV algorithm obtained 52.4% (43.6%-61.2%) sensitivity, 99.1% (98.2%-100.0%) specificity, 94.2% (88.7%-99.7%) PPV, 88.2% (85.3%-91.0%) NPV, and 88.9% (86.3%-91.5%) accuracy. CONCLUSION: The validity of the algorithms for identifying second events following primary treatment of oropharyngeal cancers was acceptable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".