6.12 Generating novel hypotheses in pediatric post-concussion syndrome utilizing a phenome-wide association study
Bibliographic record
Abstract
Objective Perform a phenome-wide association study (PheWAS) to discover predictors of post-concussion syndrome (PCS) in children after sport-related concussion. Design Electronic health record-based case-control study. Setting Single-institution level-1 academic trauma center in the southeast United States. Participants Patients 5–19 years of age were selected from our institution’s EHR research database of 583,481 patients. Cases of PCS (>1 symptom for >14 days) were defined using a sequential multi-layered algorithm that leveraged natural language processing within clinical documentation and billing codes. Controls suffered a concussion without PCS. We required patients to have ≥3 separate visits at least 180 days before the index event. Assessment of Risk Factors Independent variables consisting of pre-injury diagnoses were captured by phenotype-aggregated ICD-9/10 codes (PheCodes). PheWAS analysis was conducted with codes assigned 180 days prior to the index event. Outcome Measures Dependent variable was diagnosis of PCS (binary). Main Results There were 274 cases of PCS and 1,096 controls. PPV of our case algorithm was 81%. Of 202 pre-injury diagnoses, PCS was associated with pre-existing headache disorders (OR=5.30,95%CI 2.78–10.09; P=3.85E-7), sleep disorders (OR=3.08,95%CI 1.82–5.20; P=2.60E-5), gastritis/duodenitis (OR=3.57,95%CI 1.82–7.00; P=2.08E-4), or chronic pharyngitis (OR=3.34,95%CI 1.76–6.34; P=2.21E-4). Conclusions After successfully creating a computer-based algorithm to identify PCS, our PheWAS confirms the association of headache and sleep disorders with PCS. The association of PCS with prior chronic pharyngitis, gastritis and duodenitis may suggest a role for chronic inflammation as a risk factor for PCS, which represents a new line of study to better understand PCS pathophysiology and risk. This abstract has been published in full manuscript format and has the following citation: BMJ Citation https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389964/
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".