Bibliographic record
Abstract
INTRODUCTION: Eight percent of African Americans are carriers of the sickle cell trait. Some regard this as a benign anomaly, but others point to incidents of sudden exercise-related death, calling for a preliminary screening of either all athletes or those of African-American ancestry. This brief review considers the costs and benefits of such screening. EVIDENCE ACQUISITION: The Ovid/Health Star data-base was searched from 1996 to June 2015. 2014. The terms "exercise", "exercise therapy", "sports", "athletes", "physical activity/motor activity" and "physical fitness" were combined to yield 227,120 citations. Likewise, the terms "sickle cell trait", "sickle cell disease", "splenic infarction", "hemoglobin S" and "rhabdomyolysis" identified 12,325 citations. A combination of the 2 searches yielded 416 abstracts. EVIDENCE SYNTHESIS: Excluding items relating to animal research or forms of rhabdomyolysis other than sickling left 375 abstracts; 115 papers merited full examination. This material covered the risks of sickle cell trait and of screening (55 items), effects upon physical performance (31 items), cellular mechanisms (23 items), nutrition (4 items), and other topics (2 items). Supplemented material was drawn from reference lists and personal files. The tendency to sickling was provoked by excessive exercise relative to physical condition in hot or hypoxic conditions, and by local tissue acidosis, conditions that were best avoided by all athletes. The condition had little impact upon physical performance, but the relative risks of heat illness, exertional rhabdomyolysis, splenic infarction and sudden death were all increased by the sickle cell trait. The absolute number of critical incidents was nevertheless small, calling for close assessment of the costs and putative benefits of widespread screening. CONCLUSIONS: Sports physicians should be aware of the clinical picture of sickling and be prepared to treat it. Screening may be cost-effective if targeted to black athletes involved in certain sports, although it has yet to be demonstrated how far the diagnosis of sickle cell trait reduces the risk of death when exercising in an adverse environment. A better tactic may be to reduce risks for all competitors by educating athletes and their coaches to adopt an intensity of training appropriate to the individual's physical condition, to maintain full hydration, and to avoid exposure to excessive heat and hypoxia.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".