Classification criteria for cervical radiculopathy: An international e-Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it