Heterogeneity in Prediction Research: methods and applications
Bibliographic record
Abstract
William Osler noted in 1893 that \\xe2\\x80\\x9cIf it were not for the great variability between individuals, medicine might as well be a science, not an art\\xe2\\x80\\x9d. \\n \\nIn contrast, this thesis is based on the scientific paradigm that prediction models have the potential to guide medical decisions by exploiting identifiable heterogeneity across individual patients. \\n \\nPrediction research focuses on the development of well performing prediction models and on the assessment of their generalizability and applicability. Several methods to measure prediction model performance across clusters of patients are proposed in PART I of this thesis. PART II contains novel methods for development and validation of models that incorporate heterogeneity of treatment effect across patients. In PART III, methods for development and validation of prediction models are applied to several case studies in cardiovascular medicine, oncology, and public health.
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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 | 0.151 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".