Development of a Structured Interview for the American Congress of Rehabilitation Medicine Diagnostic Criteria for Mild Traumatic Brain Injury
Bibliographic record
Abstract
In 2023, the American Congress of Rehabilitation Medicine Brain Injury Interdisciplinary Special Interest Group (ACRM BI-ISIG) Mild Traumatic Brain Injury (TBI) Task Force published updated diagnostic criteria for mild TBI. These criteria were developed in collaboration with a panel of 32 subject matter experts in mild TBI using the Delphi method. The 2023 ACRM diagnostic criteria marked the first update since 1993, incorporating three decades of research advancements in our understanding of mild TBI. To facilitate the consistent use of the new diagnostic criteria, the ACRM BI-ISIG Mild TBI Task Force initiated a special project in September 2023 to develop a structured interview to apply the ACRM diagnostic criteria for mild TBI in clinical and research settings. The purpose of this article is to describe the development of the ACRM Structured TBI Interview and the accompanying documents. The ACRM Structured TBI Interview was developed in four phases: (1) initial development of a draft interview by two project leads, (2) review and revision over three rounds by 17 members of the ACRM BI-ISIG Mild TBI Task Force, (3) external review by 19 subject matter experts in mild TBI, and (4) field testing of the ACRM Structured TBI Interview by 11 interviewers who completed 25 diagnostic interviews. In addition to the ACRM Structured TBI Interview, three other documents were developed to help facilitate the administration of the interview (Administration Guide) and to apply the diagnostic criteria (Diagnostic Coding Form and Diagnostic Flow Diagram). A Short Form was also developed for use in contexts where administering the full structured interview is not feasible due to time constraints.
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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 | 0.042 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".