On Needles and Haystacks: The Perils of Cardiovascular Risk Screening in Rheumatoid Arthritis
Bibliographic record
Abstract
Finding a needle in a haystack is a proverbially difficult task. The chance of finding a needle is increased if the haystack is made up of a substantial proportion of needles, unless the needles are identical in appearance to the hay. This illustrates a challenge in the development of risk prediction models for cardiovascular disease (CVD) events in rheumatoid arthritis (RA). Although there are many needles in the RA population (i.e., individuals with elevated CVD risk appropriate for early preventive intervention), many do not appear to be high risk based on traditional CVD risk factors. As a telling illustration, Crowson, et al 1 demonstrated that 16% of the 525 patients with RA from Olmsted County, Minnesota, USA, experienced a CVD event during 10 years of followup. However, the 10-year Framingham risk score only predicted that 8.7% would have an event, a striking difference that illustrates the potential contribution of nontraditional risk factors, such as those related to RA disease activity and severity, to CVD risk. When almost 50% of the needles look like hay, how do you separate them? An early approach proposed was to multiply an individual’s CVD risk score by 1.5, based on the oft-cited average relative increase in CVD events for RA compared with the general population2,3. Although based on expert opinion and not validated using data from a longitudinal cohort, this strategy was recommended by a guideline committee organized by the European League Against Rheumatism Standing Committee for Clinical Affairs. In its first guideline published in 20104, the committee recommended using the 1.5 multiplication of the CVD risk score … Address correspondence to Dr. J.T. Giles. E-mail: jtg2122{at}columbia.edu
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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 | 0.033 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.024 |
| 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".