Can visible signs predict concussion diagnosis in the National Hockey League?
Bibliographic record
Abstract
BACKGROUND: Early identification and evaluation of concussions is critical. We examined the utility of using visible signs (VS) of concussion in predicting subsequent diagnosis of concussion in NHL players. METHODS: VS of concussion were identified through video review. Coders were trained to detect and record specific visual signs while viewing videos of NHL regular season games. 2460 games were reviewed by at least two independent coders across two seasons. The reliability, sensitivity and specificity of these VS were examined. RESULTS: VS were reliably coded with inter-rater agreement rates ranging from 73% to 98.9%. 1215 VS were identified in 861 events that occurred in 735 games. 47% of diagnosed concussions were associated with a VS but 53% of diagnosed concussions had no VS. Of the VS, only loss of consciousness, motor incoordination, and blank/vacant look had positive likelihood ratios greater than 1, indicating a positive association with concussion diagnoses. Slow to get up and clutching of the head were observed frequently but had low positive predictive values. Sensitivity decreased and specificity increased when multiple VS occurred together. CONCLUSIONS: Non-medical personnel can be trained to reliably identify events in which VS occur and to reliably identify specific VS within each of those events. VS can be useful to detect concussion early but they are not enough since more than half of physician diagnosed concussions occurred without the presence of a visual sign. The results underscore the complexity of this injury and highlight the need for comprehensive approaches to injury detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".