Methods for identifying health state transitions from administrative data: the case of metastasis in prostate cancer
Bibliographic record
Abstract
IntroductionHealth administrative data are a rich source of population-based information, useful for building state transition models for medical decision making. These models require identification of health state transitions and associated times. Indirect methods are needed to predict this information, as it is rarely available in administrative data.
 Objectives and ApproachWe considered a set of criteria to identify transitions to metastasis for prostate cancer patients in administrative data, utilizing dates of diagnostic and medical billing codes for secondary malignancy, palliative radiation therapy, chemotherapy and bone disorders or procedures. We evaluated the criteria using the true date of metastasis from medical charts of 195 patients linked to health care administrative data in Ontario, Canada. We also built a recursive partitioning tree to optimally combine these criteria and construct rules for identifying metastatic patients. For the evaluation, both misclassification and discrepancy between true and predicted dates for the true positives were considered.
 ResultsCriteria involving chemotherapy drugs or hospital visits with secondary malignancy ICD10 diagnosis gave the best results, with high sensitivity and specificity. Criteria involving bone related problems, radiation therapy or diagnosis of metastatic cancer in physician billing data were very specific but not sensitive. The criterion involving prescriptions for narcotics was sensitive but not specific. The fitted tree was parsimonious involving only two of the criteria, while improving the accuracy over individual criteria. Most criteria gave a “delayed” prediction, with criterion based on chemotherapy giving on average the smallest delay, as well as exhibiting the least variability. Criteria involving narcotics and bone related problems predicted metastasis date very prematurely, probably triggered by conditions other than prostate cancer.
 Conclusion/ImplicationsSeveral criteria from administrative databases satisfactorily classified prostate cancer patients with metastasis. A classification tree was built and improved the results over single criteria, demonstrating the added benefits in using advanced statistical learning methods for this task. However, “transition to metastasis” dates were predicted inaccurately, often with significant delay.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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".