Comparing 2 Whiplash Grading Systems to Predict Clinical Outcomes
Bibliographic record
Abstract
OBJECTIVE: Two whiplash severity grading systems have been developed: Quebec Task Force on Whiplash-Associated Disorders (QTF-WAD) and the Croft grading system. The majority of clinical studies to date have used the modified grading system published by the QTF-WAD in 1995 and have demonstrated some ability to predict outcome. But most studies include only injuries of lower severity (grades 1 and 2), preventing a broader interpretation. The purpose of this study was assess the ability of these grading systems to predict clinical outcome within the context of a broader injury spectrum. METHODS: This study evaluated both grading systems for their ability to predict the bivalent outcome, recovery, within a sample of 118 whiplash patients who were part of a previous case-control designed study. Of these, 36% (controls) had recovered, and 64% (cases) had not recovered. The discrete bivariate distribution between recovery status and whiplash grade was analyzed using the 2-tailed cross-tabulation statistics. RESULTS: Applying the criteria of the original 1993 Croft grading system, the subset comprised 1 grade 1 injury, 32 grade 2 injuries, 53 grade 3 injuries, and 32 grade 4 injuries. Applying the criteria of the modified (QTF-WAD) grading system, there were 1 grade 1 injury, 89 grade 2 injuries, and 28 grade 3 injuries. Both whiplash grading systems correlated negatively with recovery; that is, higher severity grades predicted a lower probability of recovery, and statistically significant correlations were observed in both, but the Croft grading system substantially outperformed the QTF-WAD system on this measure. CONCLUSIONS: The Croft grading system for whiplash injury severity showed a better predictive measure for recovery status from whiplash injuries as compared with the QTF-WAD grading system.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".