Secondary analysis of a James Lind Alliance priority setting partnership to facilitate knowledge translation in degenerative cervical myelopathy (DCM): insights from AO Spine RECODE-DCM
Bibliographic record
Abstract
OBJECTIVES: To explore whether a James Lind Alliance Priority Setting Partnership could provide insights on knowledge translation within the field of degenerative cervical myelopathy (DCM). DESIGN: Secondary analysis of a James Lind Alliance Priority Setting Partnership process for DCM. PARTICIPANTS AND SETTING: DCM stake holders, including spinal surgeons, people with myelopathy and other healthcare professionals, were surveyed internationally. Research suggestions submitted by stakeholders but considered answered were identified. Sampling characteristics of respondents were compared with the overall cohort to identify subgroups underserved by current knowledge translation. RESULTS: The survey was completed by 423 individuals from 68 different countries. A total of 22% of participants submitted research suggestions that were considered 'answered'. There was a significant difference between responses from different stakeholder groups (p<0.005). Spinal surgeons were the group which was most likely to submit an 'answered' research question. Respondents from South America were also most likely to submit 'answered' questions, when compared with other regions. However, there was no significant difference between responses from different stakeholder regions (p=0.4). CONCLUSIONS: Knowledge translation challenges exist within DCM. This practical approach to measuring knowledge translation may offer a more responsive assessment to guide interventions, complementing existing metrics.
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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 | 0.146 | 0.248 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".