Parent-Child Agreement on Postconcussion Symptoms in the Acute Postinjury Period
Bibliographic record
Abstract
OBJECTIVES: To evaluate parent-child agreement on postconcussion symptom severity within 48 hours of injury and examine the comparative predictive power of a clinical prediction rule when using parent or child symptom reporting. METHODS: Both patients and parents quantified preinjury and current symptoms using the Postconcussion Symptom Inventory (PCSI) in the pediatric emergency department. Two-way mixed, absolute measure intraclass correlation coefficients were calculated to evaluate the agreement between patient and parent reports. A multiple logistic regression was run with 9 items to determine the predictive power of the Predicting and Preventing Postconcussive Problems in Pediatrics clinical prediction rule when using the child-reported PCSI. Delong’s receiver operating characteristic curve analysis was used to compare the area under the curve (AUC) for the child-report models versus previously published parent-report models. RESULTS: Overall parent-child agreement for the total PCSI score was fair (intraclass correlation coefficient = 0.66). Parent-child agreement was greater for (1) postinjury (versus preinjury) ratings, (2) physical (versus emotional) symptoms, and (3) older (versus younger) children. Applying the clinical prediction rule by using the child-reported PCSI maintained similar predictive power to parent-reported PCSI (child AUC = 0.70 [95% confidence interval: 0.67–0.72]; parent AUC = 0.71 [95% confidence interval: 0.68–0.74]; P = .23). CONCLUSIONS: Overall parent-child agreement on postconcussion symptoms is fair but varies according to several factors. The findings for physical symptoms and the clinical prediction rule have high agreement; information in these domains are likely to be similar regardless of whether they are provided by either the parent or child. Younger children and emotional symptoms show poorer agreement; interviewing both the child and the parent would provide more comprehensive information in these instances.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".