The use and role of sport chiropractors in the National Football League: A short report
Bibliographic record
Abstract
OBJECTIVE: To analyze chiropractic utilization on National Football League (NFL) medical teams and the role played by chiropractors. DESIGN: Postal survey of head athletic trainers of the 36 teams. Survey questions were developed from responses to a questionnaire submitted to a pilot group of 30 sport chiropractors and a panel of 20 postdoctoral faculty of the sport chiropractic program of the American Chiropractic Board of Sport Physicians, as well as a representative from the University of South Alabama. RESULTS: Twenty-two of 36 questionnaires were returned for a return rate of 66%. Of the trainers who did respond, 45% have personally been treated by a chiropractor, and 55% have not. Seventy-seven percent of the trainers have referred to a chiropractor for evaluation or treatment, and 23% have not. Thirty-one percent of NFL teams use a chiropractor in an official capacity on their staffs, and 69% do not. When asked to identify conditions appropriate for referral to a chiropractor, the respondents identified low back pain (61%), "stingers" and "burners" usually associated with neck injury (31%), headaches (8%), asthma or other visceral disorders (0%). All respondents (100%) agree that some players use chiropractic care without referral from team medical staff. CONCLUSION: There is significant chiropractic participation in US professional football. Certified athletic trainers see a role for the sport chiropractor in the NFL, primarily as a spinal specialist treating low back and other musculoskeletal injuries. A substantial majority of NFL trainers have developed cooperative relationships with chiropractors, with 77% having referred a player to a chiropractor. Thirty-one percent of NFL teams have a chiropractor officially on staff, and an additional 12% of teams refer players to chiropractors but do not directly retain these chiropractors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".