Comparison of Methods for Classifying Persistent Post-Concussive Symptoms in Children
Bibliographic record
Abstract
Pediatric mild traumatic brain injury (pmTBI) has received increased public scrutiny over the past decade, especially regarding children who experience persistent post-concussive symptoms (PPCS). However, several methods for defining PPCS exist in clinical and scientific literature, and even healthy children frequently exhibit non-specific, concussive-like symptoms. Inter-method agreement (six PPCS methods), observed misclassification rates, and other psychometric properties were examined in large cohorts of consecutively recruited adolescent patients with pmTBI (n = 162) 1 week and 4 months post-injury and in age/sex-matched healthy controls (HC; n = 117) at equivalent time intervals. Six published PPCS methods were stratified into Simple Change (e.g., International Statistical Classification of Diseases and Related Health Problems, 10th revision [ICD-10]) and Standardized Change (e.g., reliable change indices) algorithms. Among HC, test-retest reliability was fair to good across the 4-month assessment window, with evidence of bias (i.e., higher symptom ratings) during retrospective relative to other assessments. Misclassification rates among HC were higher (>30%) for Simple Change algorithms, with poor inter-rater reliability of symptom burden across HC and their parents. A 49% spread existed in terms of the proportion of pmTBI patients “diagnosed” with PPCS at 4 months, with superior inter-method agreement among standardized change algorithms. In conclusion, the self-reporting of symptom burden is only modestly reliable in typically developing adolescents over a 4-month period, with additional evidence for systematic bias in both adolescent and parental ratings. Significant variation existed for identifying pmTBI patients who had “recovered” (i.e., those who did not meet individual criteria for PPCS) from concussion across the six definitions, representing a considerable challenge for estimating the true incidence rate of PPCS in published literature. Although relatively straightforward to obtain, current findings question the utility of the most commonly used Simple Change scores for diagnosis of PPCS in clinical settings.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".