Evaluation of a prioritization system for mild traumatic brain injury case ascertainment from emergency department records
Bibliographic record
Abstract
Abstract Objective To evaluate modifications to a tiered priority chart review system designed to efficiently identify patients with mild traumatic brain injury (mTBI), via medical chart review, that presented to an emergency department or urgent and primary care center (ED/UPCC) for research purposes. Methods We initially created a tiered priority chart review system and applied it to screening N=17,072 electronic ED/UPCC medical charts in study 1 ( Clinicaltrials.gov ID# NCT04704037 ). Chief complaints with high positive predictive value (PPV) for correctly identifying possible/probable mTBI cases were moved to a higher tier and those with low positive predictive value were downgraded to create a tiered priority chart review system. This revised system was then used in a second research study ( Clinicaltrials.gov ID# NCT05365776 ), and PPV values were calculated for the new sample (N=4,434). The original and revised tiered priority system were compared with respect to overall efficiency. Results PPV for specific chief complaint key terms varied markedly from 0% to 61% and resulted in an empirically-driven resorting of the priority tiers. After excluding clearly ineligible participants, 49% of charts reviewed in the first study and 60% of charts reviewed in the second study were identified as a possible or probable mTBI. This indicates an improvement in overall efficiency (12%; χ 2 (1)=114.7, p<.001) compared to the original system. Conclusion The revised tiered priority chart review system was more efficient at identifying patients with mTBI for the purposes of mTBI study recruitment.
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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 | 0.056 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".