Criteria to Screen for Traumatic Cervical Spine Instability: A Consensus of Chiropractic Radiologists
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to establish consensus on a radiographic definition for cervical instability for routine use in chiropractic patients who sustain trauma to the cervical spine. METHOD: We conducted a modified Delphi study with a panel of chiropractic radiologists. Panelists were asked to rate potential screening criteria for traumatic cervical spine instability when assessing cervical spine radiographs. Items rated as important for inclusion by at least 60% of participants in round 1 were submitted for a second round of voting in round 2. Items rated for inclusion by at least 75% of the participants in round 2 were used to create the consensus-based list of screening criteria. Participants were asked to vote and reach agreement on the final screening criteria list in round 3. RESULTS: Twenty-nine chiropractic radiologists participated in round 1. After 3 rounds of survey, 85% of participants approved the final consensus-based list of criteria for traumatic cervical spine instability screening, including 6 clinical signs and symptoms and 5 radiographic criteria. Participants agreed that the presence of 1 or more of these clinical signs and symptoms and/or 1 or more of the 5 radiographic criteria on routine static radiographic studies suggests cervical instability. CONCLUSION: The consensus-based radiographic definition of traumatic cervical spine instability includes 6 clinical signs and symptoms and 5 radiographic criteria that doctors of chiropractic should apply to their patients who sustain trauma to the cervical spine.
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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 | 0.087 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".