A Comparison of Empirical Ranking Methods of Frequency and Severity Ratings of Clinical Presentations
Bibliographic record
Abstract
This study compares five methods of ranking Likert ratings on frequency and severity scales. Data were drawn from an international online survey conducted as part of a practice analysis with 91 diplomates of the American Chiropractic Board of Radiology. A total of 129 clinical presentations had been rated on two scales. The frequency scale specifies how often each condition is seen in practice from 1 (never) to 5 (daily). The severity scale indicates how severe each condition is to the patient from 1 (no risk) to 5 (severe risk). These ratings were used in five methods of ranking to identify the 10 most important conditions reported by chiropractic radiologists. The similarity of ranks across the five methods was then analyzed with Spearman's rank correlations. The results of the study indicate that the Rasch model provides the most precise results about the importance of conditions seen in practice.
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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 | 0.082 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".