Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The objective of this study was to identify the level of grant funding for chronic rhinosinusitis (CRS) with the purpose of elucidating if disparities exist compared to other common chronic diseases. STUDY DESIGN: Review of four major health research grant agencies from the United States (National Institutes of Health), Canada (Canadian Institute of Health Research), and United Kingdom (National Institute of Health Research and Medical Research Council). METHODS: Research operating grants awarded in the fields of CRS, asthma, diabetes, and dementia were identified using database-specific search strategies. Searches were limited to the previous 10 years (2004-2014). Comparator chronic diseases were chosen to have similar prevalence rates and low mortality risk. Research efficiency was calculated as the monetary value of grants awarded per paper published. RESULTS: There is a large disparity in the number of grants awarded for research in CRS (n = 196; $74,774,384), asthma (n = 13,226; $8,358,861,941), diabetes (n = 54,902; $47,282,739,735), and dementia (n = 34,569; $16,709,900,125). In terms of research efficiency, CRS researchers received eight to 12 times less financial support per paper published compared to those in our comparator conditions. CONCLUSIONS: This study has demonstrated that over the last 10 years, CRS is disproportionately underfunded (∼$75 million) compared to other similarly prevalent chronic diseases such as asthma (∼$8.3 billion), diabetes (∼$47.2 billion), and dementia (∼$16.7 billion). We feel this justifies further research into identifying and reducing barriers to obtaining grant support for CRS. LEVEL OF EVIDENCE: NA.
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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 | 0.048 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".