Two Nearest Means: A New Case Based Reasoning Method
Bibliographic record
Abstract
Objective: Case-based reasoning predicts outcomes based on matching to training cases and without modeling the relationship between features and outcome. This study compares the accuracy of the two nearest means (2NM), a case-based reasoning, to regression, a feature-based reasoning. Data Sources: The accuracy of the two methods was examined in predicting mortality of 296,051 residents in Veterans Health Affairs nursing homes. Data was collected from 1/1/2000 to 9/10/2012. Data was randomly divided into training (90%) and validation (10%) samples. Study Design: Case-control observational study Data Collection/Extraction Methods: In the 2NM algorithm, first data was transformed so that all features are monotonely related to the outcome. Second, all means that violate monotone order were set aside; to be processed as exceptions to the general algorithm. Third, for predicting a new case, the means in the training set are divided into “excessive” and “partial” means, based on how they match a new case. Finally, the outcome for the new case is predicted as the average of two means: the excessive mean with minimum outcome and the partial mean with maximum outcome. For regression, we predicted mortality from age, gender, and 10 disabilities. Principal Findings : In cases set aside for validation, the 2NM had a McFadden Pseudo R-squared of 0.51. The linear logistic regression, trained on the same training sample and predicting to the same validation cases, had a McFadden Pseudo R-squared of 0.09. The 2NM was significantly more accurate (alpha <0.001) than linear logistic regression. Conclusions : 2NM, a Case-Based reasoning method, captured nonlinear interactions in the data.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".