A decision aiding methodology to compare patient classification systems
Bibliographic record
Abstract
Spanish public hospitals funding depends on the classification of attended pathologies. These pathologies are grouped thanks to a patient classification system (PCS) into the so-called diagnostic related groups (DRGs). International Classification of Diseases (ICD) coding systems generate various DRGs. Based on the ICD-9, the all patients diagnostic related groups (AP-DRGs) were used. In January 2016, Spain adopted ICD-10. One of the casualties from this transition was the AP-DRG PCS which did not accept information coded with ICD-10. A new system had to be adopted. In this paper we develop a methodology to assist decision-makers who are charged with evaluating and determining replacement PCS in the context of scarce resources and limited hospital funding. We ground our approach in a rigorous analytical framework is order to understand the pros and cons of candidate systems, from both clinical and managerial perspectives. We propose a multicriteria decision-aiding approach based on the outranking method, ELECTRE III to produce a ranking of the different options. The methodology is applied to a case study of a Spanish hospital where the change from ICD-9 to ICD-10 has already occurred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".