Evaluating the utility of ICD-10 diagnostic criteria for postconcussion syndrome following mild traumatic brain injury
Bibliographic record
Abstract
The present study investigated the utility of the International Classification of Diseases and Related Health Problems, 10th edition (ICD-10) diagnostic criteria for postconcussion syndrome (PCS) symptoms by comparing symptom endorsement rates in a group of patients with mild traumatic brain injury (MTBI) to those of a noninjured control group at one month and three months post-injury. The 110 MTBI patients and 118 control participants were group-matched on age, gender, and education level. Seven of the nine self-reported ICD-10 PCS symptoms differentiated the groups at one month post-injury and two symptoms differentiated the groups at three months post-injury: symptom endorsement rates were higher in the MTBI group at both time periods. Fatiguing quickly and dizziness/vertigo best differentiated the groups at both time periods, while depression and anxiety/tension failed to differentiate the groups at either time period. Collectively, the ICD-10 PCS symptoms accurately classified the MTBI patients at one month post-injury, with the optimal positive test threshold of endorsement of five symptoms coinciding with a sensitivity and specificity of 73% and 61%, respectively. The ICD-10 PCS symptoms were unable to accurately classify the MTBI patients at three months post-injury.
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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 | 0.020 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".