An Evaluation of the Patient Clinical Complexity Level (PCCL) Method for the Complexity Adjustment in the Korean Diagnosis-Related Groups (KDRG)
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Bibliographic record
Abstract
Abstract Objective To evaluate the performance of the Patient Clinical Complexity Level (PCCL) mechanism, which is the patient level complexity adjustment factor within the Korean Diagnosis-Related Groups (KDRG) patient classification system, for explaining the variation of resource consumption within Age Adjacent Diagnosis-related groups (AADRGs). Methods We used the inpatient claims data from a public hospital in Korea from January 1, 2017 to June 30, 2019, with 18,846 claims and 138 Age Adjacent Diagnosis-related groups (AADRGs). The differences in the total average payment between the four PCCL levels for each AADRG was tested using ANOVA and Duncan’s post-hoc test. The three patterns of the differences with R-squared were: the PCCL reflected the complexity well (Valid); the average payment of PCCL 2, 3, 4 was greater than PCCL 0 (Partially Valid); the PCCL did not reflect the complexity (Not Valid). Results There were 9 (6.52%), 26 (18.84%), and 103 (74.64%) ADRGs included in VALID, PARTIALLY VALID and NOT VALID, respectively. The average R-squared in VALID, PARTIALLY VALID, and NOT VALID was 32.18%, 40.81%, and 35.41% respectively, with the average R-squared for all patterns of 36.21%. Conclusions Adjusting using PCCL in the KDRG classification system exhibited low performance to explain the variation of resource consumption within Age Adjacent Diagnosis-related groups (AADRGs). As the KDRG classification system is used for reimbursement under the New DRG-based PPS pilot project with plans for expansion, there should be an overall review of the validity of the complexity and rationality of using the KDRG classification system.
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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 | 0.236 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.019 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it