Mismatches in risk stratification between the UK and Canadian CF cohorts.
Bibliographic record
Abstract
<p>Two prognostic models were constructed by AutoPrognosis separately on the UK and Canadian CF populations. Canadian CF patients with future outcomes of survival, death and LTx were annotated with circles, crosses, and triangles, respectively. Their locations were determined by the output of the two AutoPrognosis models. Very high-risk patients with FEV<sub>1</sub> below 30% predicted and supplement oxygenation were highlighted with cyan circles. The two AutoPrognosis models were in agreement of risk stratum for most of these very high-risk patients. Mismatches happened in two subgroups of patients with underestimated risk levels. The first subgroup consisted of moderate-risk patients that were identified as low-risk by the AutoPrognosis model developed on the UK population and was referred to as mismatch 1. The latter subgroup consisted of high-risk patients that fell into the moderate-risk stratum in the UK population. We referred to the corresponding area as mismatch 2.</p>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.209 | 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".