Sickle cell trait, exertion-related death and confounded estimates
Bibliographic record
Abstract
Sickle cell trait (SCT) has historically been thought of as a benign condition. However, there has been increasing recognition that, in athletes, SCT is associated with an elevated risk for exertion-related death (ERD).1 With the hope to minimise future tragedies, Harmon et al 2 sought to quantify the association of SCT and ERD. They looked at data compiled on nearly two million collegiate ‘athlete-years’ between 2004 and 2008. Since the risk associated with SCT was highest among Division 1 (D1) football players, the authors elected to focus on that group. Their highlighted conclusions, ‘Sickle cell trait associated with a RR of death of 37 times…’, are now being referenced in discussions regarding SCT testing.3 ,4 Others may have concerns about generalising results from D1 athletes to all athletes or lack of discussion about the small number of deaths (from a statistical standpoint) and thus uncertainty surrounding the results. Our concern is that the conclusions based on combining data from all race/ethnicities are not meaningful because of confounding bias secondary to race/ethnicity. Among the D1 footballers, the researchers found 1 ERD in every 827 athlete-years in those who had SCT. Although the issue of race/ethnicity is an uncomfortable topic, given …
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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 | 0.014 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".