REAL OPTIONS THEORY AND CLASSIFICATION OF PATIENTS BY DIAGNOSIS RELATED GROUPS: HOW THESE DIFFERENT FIELDS COULD RELATE?
Bibliographic record
Abstract
ABSTRACT In a complex environment, the managers of hospital organizations should take hard decisions all the time. Therefore, tools and techniques, which seek to understand the past and project the future, are very important. In some situations, the complexity encountered requires the transfer of knowledge from other areas, to find solutions and develop tools that provide efficient management of resources. In this scenario, this article has the main objective to present a theoretical discussion that brings the relationship between the Theory of Real Options and the Diagnosis Related Groups, to identify possible points that underlie the use of real options in Diagnosis Related Groups. The results demonstrate that, with the patient's condition as the focus, both are applied in the hospital environment with the objective of supporting decision-making, but not together. In addition, the differences observed make the combination of some of its concepts relevant for decision-making.
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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 | 0.001 | 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) | 0.001 | 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 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".