Practice Analysis of Chiropractic Radiology: Identifying Items for Part I of the Clinical Competency Examination
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to describe the current scope of practice of chiropractic radiologists by identifying frequent tasks conducted as well as those conditions most often seen and those that present the greatest risk of harm to patients. METHODS: A mixed-methods approach was used. An online survey was conducted with 91 diplomates listed with the American Chiropractic Board of Radiology. Participants rated the frequency of tasks they perform and conditions they see on a 5-point scale from "never" to "daily." They also rated the level of risk each condition presents to patients on a 5-point scale from "no risk" to "severe risk." Frequency and risk ratings were then presented in rank order to 22 subject matter experts at 3 focus groups. RESULTS: The most frequent task reported was writing radiology reports (mean [SD], 4.29 [1.58]). Ratings of the frequency of conditions seen in practice and the risk they present to patients were ranked from the highest to lowest for frequency and risk separately. The most frequent conditions seen were reportedly those with structural or joint derangement; the highest risk conditions seen are those that are systemic. Focus group members recommended that some conditions receive higher rankings and that certain conditions be recategorized for future practice analyses. CONCLUSIONS: This study helps to define the current scope of practice of chiropractic radiologists and identify frequent tasks and conditions. These results inform the development of a new test outline for Part I of the chiropractic radiology certification examination to ensure that examinees are tested on the most important conditions chiropractic radiologists see in practice.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".