Author Response: Association of Position Played and Career Duration and Chronic Traumatic Encephalopathy at Autopsy in Elite Football and Hockey Players
Bibliographic record
Abstract
We completely share Dr. Dams-O’Connor's hope for replication studies and increasingly complete and accurate data.1 In our cohort of 35 former elite athletes, we did not think it surprising to have uncovered no history of moderate-severe traumatic brain injury (TBI)—as commonly defined2—nor did we find it to be in stark contrast to population-based studies. Indeed, our reading of the study cited to suggest a high (>20%) prevalence in the general population of more severe TBI is that only 192/6,998 respondents (2.7%) recollected a TBI with loss of consciousness >30 minutes—the definition used in that study for moderate-severe TBI.3 At that rate (about 1/37), it would not be unexpected to find 0% prevalence in a cohort of 35 individuals. Moreover, from the data cited on intercollegiate athletes using the Brain Injury Screening Questionnaire, showing that 12/90 recollected a TBI with loss of consciousness lasting several minutes to an hour, interpreted as clinically significant TBI, it cannot be ascertained whether any of those individuals had a moderate, as opposed to a mild, TBI.4 In our common quest for scientific rigor, it is important that the value of a structured TBI screening tool be neither understated nor overstated.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".