Classification criteria for cervical radiculopathy: An international e-Delphi study
Bibliographic record
Abstract
BACKGROUND: Establishing a set of uniform classification criteria (CC) for cervical radiculopathy (CR) is required to aid future recruitment of homogenous populations to clinical trials. OBJECTIVES: To establish expert informed consensus on CC for CR. DESIGN: A pre-defined four round e-Delphi study in accordance with the guidance on Conducting and Reporting Delphi Studies. METHODS: Individuals with a background in physiotherapy who had authored two or more peer-reviewed publications on CR were invited to participate. The initial round asked opinions on CC for CR. Content analysis was performed on round one output and a list of discrete items were generated forming the round two survey. In rounds two to four, participants were asked to rate the level of importance of each item on a six-point Likert scale. Data were analysed descriptively using median, interquartile range and percentage agreement. Items reaching pre-defined consensus criteria were carried forward to the next round. Items remaining after the fourth round constituted expert consensus on CC for CR. RESULTS: Twelve participants participated with one drop out. The final round identified one inclusion CC and 12 exclusion CC. The inclusion CC that remained achieved 82% agreement and was a cluster criterion consisting of radicular pain with arm pain worse than neck pain; paraesthesia or numbness and/or weakness and/or altered reflex; MRI confirmed nerve root compression compatible with clinical findings. CONCLUSIONS: The CC identified can be used to inform eligibility criteria for future CR trials although caution should be practiced as consensus on measurement tools requires further investigation.
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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 | 0.175 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".