Existing Funding Sources in Degenerative Cervical Myelopathy Research: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Degenerative cervical myelopathy (DCM) is a common, disabling condition of symptomatic cervical spinal cord compression that requires significant research advances to improve patient outcomes. A James Lind Alliance Partnership recently identified the top research priorities for DCM. To effectively address these priorities, appropriate funding of DCM research is essential. OBJECTIVE: The aim of this paper is to review current funding in DCM research and highlight future research funding opportunities. METHODS: A systematic search of Web of Science for "cervical AND myelopathy" was conducted. Papers exclusively studying DCM with declared funding and published between January 1, 1995, and March 21, 2020, were considered eligible. Funding sources were classified by country of origin and organization type. A grant search was also conducted using Dimensions.ai (Digital Science Ltd). RESULTS: A total of 621 papers were included, with 300 unique funding bodies. The top funders were AO Spine (n=87); National Institutes of Health, USA (n=63); and National Natural Science Foundation, China (n=63). Funding sources in the USA (n=242) supported the most DCM research, followed by China (n=209) and Japan (n=116). Funding in the USA was primarily provided by corporate or nonprofit organizations (146/242, 60.3%), while in China, the majority of funding was from institutions (208/209, 99.5%). Dimensions.ai gives an estimate for the total declared grant funding awards for DCM-specific research. Data here showed 180 grants awarded specifically for DCM research, with a total value of US $45.6 million since 1996. CONCLUSIONS: DCM funding appears to be predominantly from the USA, China, and Japan, aligning with areas of high DCM research activity and underpinning the importance of funding to increasing research capacity. The existing funding sources differ from medical research in general, representing opportunities for future investment in DCM.
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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 | 0.081 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.060 | 0.072 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".