Understanding prognostic models: The example of the PHASES score for unruptured intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Prognostic studies may inform individuals about the future course of their disease and help clinical decision making, but problems abound. METHODS: We summarize a study on the natural history of unruptured intracranial aneurysms (UIAs) and review the various steps in the construction of prognostic models. We emphasize the fundamental inductive problems of prognostic studies that attempt to use the backward road from the extension of patients suffering an event to create a new intensional definition of classes of patients at risk. RESULTS: The first step in a prognostic model is to identify candidate baseline variables to be entered into the model, according to background knowledge, previous studies, and statistical associations with the event of interest. This is a multivariate task. The modeler already knows the outcomes the model is supposed to 'predict', so that multiple models are tested against the data until a satisfactory fit is obtained. The variables used to construct the model should not be added in an ad hoc fashion to fit heterogeneous studies. They should be selected in such a fashion as to be exportable outside the study to new patients. An infinite number of models can fit the same data. Thus, the most important step is to validate the prognostic value of the model in patients that were not used to construct the model. In the case of UIAs, this has never been done. CONCLUSION: Prognostic studies present multiple problems. Unvalidated models should not be used in clinical practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".