An Inverse Optimization Approach to Measuring Clinical Pathway\n Concordance
Bibliographic record
Abstract
Clinical pathways outline standardized processes in the delivery of care for\na specific disease. Patient journeys through the healthcare system, though, can\ndeviate substantially from these pathways. Given the positive benefits of\nclinical pathways, it is important to measure the concordance of patient\npathways so that variations in health system performance or bottlenecks in the\ndelivery of care can be detected, monitored, and acted upon. This paper\nproposes the first data-driven inverse optimization approach to measuring\npathway concordance in any problem context. Our specific application considers\nclinical pathway concordance for stage III colon cancer. We develop a novel\nconcordance metric and demonstrate using real patient data from Ontario, Canada\nthat it has a statistically significant association with survival. Our\nmethodological approach considers a patient's journey as a walk in a directed\ngraph, where the costs on the arcs are derived by solving an inverse shortest\npath problem. The inverse optimization model uses two sources of information to\nfind the arc costs: reference pathways developed by a provincial cancer agency\n(primary) and data from real-world patient-related activity from patients with\nboth positive and negative clinical outcomes (secondary). Thus, our inverse\noptimization framework extends existing models by including data points of both\nvarying "primacy" and "alignment". Data primacy is addressed through a\ntwo-stage approach to imputing the cost vector, while data alignment is\naddressed by a hybrid objective function that aims to minimize and maximize\nsuboptimality error for different subsets of input data.\n
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".