The Stability of Retrospective Pre-injury Symptom Ratings Following Pediatric Concussion
Bibliographic record
Abstract
Objective: To determine the stability of children’s retrospective ratings of pre-injury levels over time following concussion. Methods: Children and adolescents (n=3,063) between the ages of 5-17 diagnosed with a concussion by their treating PED physician within 48hrs of injury completed the age-appropriate version of the Post-Concussion Symptom Inventory (PCSI) at the PED and at 1, 2, 4, 8, and 12-weeks post-injury. At each time point, participants retrospectively recalled their pre-injury levels of post-injury symptoms. Total scale, subscales (physical, cognitive, emotional, and sleep), and Individual items from the PCSI were analyzed for stability using Gini’s mean difference (GMD). Results: The mean GMD for total score was 0.31 (95% CI= 0.28, 0.34) for the PCSI-SR5, 0.19 (95% CI= 0.18, 0.20) for the PCSI-SR8, and 0.17 (95% CI= 0.16, 0.18) for the PCSI-SR13. Subscales ranged from mean GMD 0.18 (physical) to 0.31 (emotional) for the PCSI-SR8 and 0.16 (physical) to 0.31 (fatigue) for the PCSI-SR13. At the item-level, mean GMD ranged from 0.13-0.60 on the PCSI-SR5, 0.08-0.59 on the PCSI-SR8, and 0.11-0.41 on the PCSI-SR13. Conclusions: Children and adolescents recall their retrospective pre-injury symptom ratings with good-to-perfect stability over the first three-months following their concussion. Although some individual items underperformed, variability was reduced as items were combined at the subscale and full-scale level. There is limited benefit gained from collecting multiple pre-injury symptom queries. This study is registered at Clinicaltrials.gov through the US National Institute of Health/National Library of Medicine. (NCT01873287; http://clinicaltrials.gov/ct2/show/NCT01873287).
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".