James Lind Alliance Priority Setting Partnership for Degenerative Cervical Myelopathy [AO Spine RECODE-DCM]: An Overview of the Methodology Used to Process and Short-List Research Uncertainties
Bibliographic record
Abstract
STUDY DESIGN: Overview of the methods used for a James Lind Alliance (JLA) Priority Setting Partnership (PSP). OBJECTIVES: The objectives of this article are to (i) provide a brief overview of the JLA-facilitated PSP process; (ii) outline how research uncertainties were initially processed in the AO Spine RECODE-DCM PSP; and (iii) delineate the methods for interim prioritization and the priority setting workshop. METHODS: A steering group was created to define the scope for the PSP, organize its activities, and establish protocols for decision-making. A survey was created asking what questions on the diagnosis, treatment, and long-term management of DCM should be answered by future research. Results from the survey were sorted into summary questions. Several databases were searched to identify literature that already answered these summary questions. The final list of summary questions was distributed by survey for interim prioritization. Participants were asked to select the top ten most important summary questions. The questions that were ranked the highest were discussed at an in-person consensus workshop. RESULTS: The initial survey yielded a total of 3404 potential research questions. Of the in-scope submissions, 988 were related to diagnosis, 1324 to treatment, and 615 to long-term management of DCM. A total of 76 summary questions were developed to reflect the original submissions. Following a second survey, a list of the top 26 interim priorities was generated and discussed at the in-person priority setting workshop. CONCLUSIONS: PSPs enable research priorities to be identified that consider the perspectives and interests of all relevant stakeholders.
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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 | 0.311 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".