A - 11 Are Self-Reported Cognitive Symptoms an Accurate Evaluation of Cognitive Functioning?
Bibliographic record
Abstract
Abstract Objective The purpose of this study was to evaluate whether self-reports of cognitive symptoms were associated with cognitive test performances. Methods The sample included 112 Canadian Football League (CFL) athletes who were diagnosed using CFL concussion protocols. All participants underwent a cognitive assessment at baseline and prior to medical clearance. The battery included the immediate post-concussion assessment and cognitive testing (ImPACT) and The Post-Concussion Symptom Checklist. Results Self-reported cognitive symptoms and cognitive test performances were evaluated using Spearman’s rank correlations (rho; ρ). There were significant negative correlations between post-concussion verbal memory composite and the self-reported cognitive symptoms total (ρ = −0.22). Similar patterns were found for visual memory composite and the self-reported cognitive symptom total (ρ = −0.19). Paired-samples t-tests were used to assess differences between pre- and post-concussion scores. Cases were omitted if there were no pre- or post-test. If multiple concussions were sustained, the first assessment was used (n = 99). There was a significant difference between the pre- and post-test results between the subjective cognitive symptom total (t = −2.034, p > 0.05). Conclusions These outcomes suggest that CFL athletes report significantly higher cognitive symptoms following a concussion. Additionally, the pre-test subjective measures were not correlated to objective cognitive functioning. However, post-concussion subjective measures were negatively correlated with verbal and visual memory. This suggests that self-reports were more accurate at assessing their overall functioning following a concussion.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".