Injury Severity Score coding: Data analyst v. emerging m-health technology
Bibliographic record
Abstract
BACKGROUND: The cost of Abbreviated Injury Scale (AIS) coding has limited its utility in areas of the world with the highest incidence of trauma. We hypothesised that emerging mobile health (m-health) technology could offer a cost-effective alternative to the current gold-standard AIS mechanism in a high-volume trauma centre in South Africa. METHODS: A prospectively collected sample of consecutive patients admitted following a traumatic injury that required an operation during a 1-month period was selected for the study. AISs and Injury Severity Scores (ISSs) were generated by clinician-entered data using an m-health application (ISS eTHR) as well as by a team of AIS coders at Vancouver General Hospital, Canada (ISS VGH). Rater agreements for ISSs were analysed using Bland-Altman plots with 95% limits of agreement (LoA) and kappa statistics of the ISSs grouped into ordinal categories. Reliability was analysed using a two-way mixed-model intraclass correlation coefficient (ICC). Calibration and discrimination of univariate logistic regression models built to predict in-hospital complications using ISSs coded by the two methods were also compared. RESULTS: Fifty-seven patients were managed operatively during the study period. The mean age of the cohort was 27.2 years (range 14 - 62), and 96.3% were male. The mechanism of injury was penetrating in 93.4% of cases, of which 52.8% were gunshot injuries. The LoA fell within -8.6 - 9.4. The mean ISS difference was 0.4 (95% CI -0.8 - 1.6). The kappa statistic was 0.53. The ICC of the individual ISS was 0.88 (95% CI 0.81 - 0.93) and the categorical ISS was 0.81 (95% CI 0.68 - 0.87). Model performance to predict in-hospital complications using either the ISS eTHR or the ISS VGH was equivalent. CONCLUSIONS: ISSs calculated by the eTHR and gold-standard coding were comparable. Emerging m-health technology provides a cost-effective alternative for injury severity scoring.
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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".