Heterogeneity in Prediction Research: methods and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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