Are U‐shaped relationships between risk factors and outcomes artifactual?
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate whether the observed nadir in a U- or J-shaped relationship between a particular risk factor and a future health outcome is a function of the distribution of the risk factor in the sample being analyzed. METHODS: Data from the ORIGIN trial were used to assess the relationship between three risk factors (weight, systolic blood pressure, and serum insulin) and the hazard of a major cardiovascular event comprising a nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. Three spline curves were generated for each risk factor. The first was based on all available data, the second for a subgroup with a higher mean risk factor level, and the third for a subgroup with a lower mean risk factor level. Nadir levels of the risk factor (i.e., risk factor levels predicting the lowest hazard) were then identified for each spline curve. RESULTS: When compared to the nadir values based on all available data, nadir values for all three risk factors were higher for the subgroups with higher mean levels and lower for those with lower mean levels. CONCLUSIONS: The distribution of a risk factor in the population is an important determinant of its nadir value. Populations with high or low values may have high and low nadirs, respectively. Identification of a nadir for a modifiable risk factor from epidemiologic relationships may therefore arise from this distribution bias and is therefore unrelated to therapeutic targets.
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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 | 0.002 |
| 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) | 0.000 | 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".