Evaluation of the Injury Impairment Scale, a Tool to Predict Road Crash Sequelae, in a French Cohort of Road Crash Survivors
Bibliographic record
Abstract
OBJECTIVE: The objective of the present study was to validate sequelae prediction by the Maximal Injury Impairment Score (M-IIS) in comparison with the Functional Independence Measure (FIM) assessed at 1-year follow-up of severe road crash victims. METHODS: The study population came from "the Etude et Suivi d'une Population d'Accidentés de la Route dans le Rhône" (ESPARR; Rhône Area Road Crash Victim Follow-up Study) cohort: 178 victims (with Maximal Abbreviated Injury Scale ≥ 3) of road crashes in the Rhône administrative department of France, aged ≥ 16 years and with medical examination including FIM scoring 1 year postaccident. Two thresholds were tested for both scores. Firstly, the relation between FIM and M-IIS was assessed on logistic regression models adjusted on age and presence of complications at 1 year postaccident. The predictive capacity of M-IIS was expressed as its negative and positive predictive values and was considered good when 80 percent or better. RESULTS: Sixty-three of the 178 adult subjects (mean age = 37.7 years; range = 16.1-82.9 years) showed postaccident complications. One-year sequelae prediction on M-IIS was greater in head, spine, and limb lesions but limited to slight impairments (M-IIS = 1). There was a significant correlation between FIM and M-IIS, although age and medical complications were confounding factors on certain multivariate models. The predictive capacity of M-IIS was low for all types of sequelae. CONCLUSIONS: M-IIS, in this severely injured population, failed to predict sequelae at 1 year as measured by the FIM, despite a good correlation between the two. Complications are to be taken into account in assessing the M-IIS's capacity to predict sequelae. Further evaluation will be needed on larger series or assessment of other indicators and measures of sequelae at 1 year to obtain a robust tool to predict road crash sequelae.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".