3.2 Development of SCAT5 reliable change metrics in professional hockey
Bibliographic record
Abstract
Objective The purpose of this study was to create SCAT5 reliable change scores in a large sample of professional hockey players. Design Longitudinal cohort. Setting National Hockey League (NHL) pre-season medical evaluations. Participants All NHL players were given the NHL modified SCAT5 prior to both the 2018–19 and 2019–20 seasons. Valid 10-word List Learning (n=986), Delayed List Recall (n=986), modified Balance Error Scoring System (n=828), and Concentration (n=987) data were extracted for players with 2 consecutive valid baselines and no intervening suspected concussion evaluations. Outcome Measures Paired t-tests were used to examine differences between time 1 and time 2 performance. Pearson correlations were used to assess test-retest reliability and reliable change was calculated using the Iverson method. Main Results Correlations between repeat administrations had mostly small to moderate effects (r =.29 to .51). Due to significant list-learning form differences, .80, .90, .95, and .99 confidence interval reliable change metrics were developed for 3 list administration orders (1,2; 2,3; 3,1) for English Preference and non-English preference speakers. Reliable change metrics and their associated base rates are presented for mBESS and concentration subscores. Conclusions This study offers 1-year test-retest reliability and reliable change metrics for a large group of English and non-English preference professional hockey players. Data represent an initial step in evaluating the psychometric properties of the SCAT5 10-word list and Concentration indices – as well as providing reliable change guidelines to aid instrument interpretation. This abstract has been published in full manuscript format and has the following citation: BMJ Citation https://bjsm.bmj.com/content/early/2022/03/24/bjsports-2021-104851.long
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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 | 0.051 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".