A phenome-wide association study identifying risk factors for pediatric post-concussion syndrome
Bibliographic record
Abstract
Abstract Objective To identify risk factors and generate hypotheses for pediatric post-concussion syndrome (PCS) using a phenome-wide association study (PheWAS). Methods A PheWAS (case-control) was conducted following the development and validation of a novel electronic health record-based algorithm that identified PCS cases and controls from an institutional database of >2.8 million patients. Cases were patients ages 5-18 with PCS codes or keywords identified by natural language processing of clinical notes. Controls were patients with mild traumatic brain injury (mTBI) codes only. Patients with moderate or severe brain injury were excluded. All patients used our healthcare system at least three times 180 days before their injury. Exposures included all pre-injury medical diagnoses assigned at least 180 days prior. Results The algorithm identified 274 pediatric PCS cases (156 females) and 1,096 controls that were age and sex matched to cases. Cases and controls both had a mean of >8 years of healthcare system use pre-injury. Of 202 pre-injury medical, four were associated with PCS after controlling for multiple testing: headache disorders (OR=5.3; 95%CI 2.8-10.1; P= 3.8e-7), sleep disorders (OR=3.1; 95%CI 1.8-5.2; P= 2.6e-5), gastritis/duodenitis (OR=3.6, 95%CI 1.8-7.0; P= 2.1e-4), and chronic pharyngitis (OR=3.3; 95%CI 1.8-6.3; P= 2.2e-4). Conclusions These results confirm the strong association of pre-injury headache disorders with PCS and provides evidence for the association of pre-injury sleep disorders with PCS. An association of PCS with prior chronic gastritis/duodenitis and pharyngitis was seen that suggests a role for chronic inflammation in PCS pathophysiology and risk. These factors should be considered during the management of pediatric mTBI cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".