Trends in sinusitis research: a systematic review of extramural funding
Bibliographic record
Abstract
BACKGROUND: Innovation represents a core value of the American Rhinologic Society (ARS), with multiple efforts to promote research in the advancement rhinologic care. We therefore sought to identify trends in extramural sinusitis funding and underutilized sources of support to facilitate future efforts. METHODS: A systematic review of the National Institutes of Health (NIH) Research Portfolio Online Tools (RePORTER) database (fiscal year 1993 to 2017) was completed with the search strategy: ("chronic sinusitis" OR rhinosinusitis). All identified studies were accepted for review, with comparison to ARS membership rolls to identify studies supported by ARS investigators. Foundation awards were surveyed to identify and characterize additional sources of support. RESULTS: The systematic review identified 958 projects receiving NIH funding, of which 120 remain active. The percentage of sinusitis-related awards and total funding relative to all NIH awards increased over the past 10 years (2006 to 2016) from 0.06% (8 / 9128) and 0.09% ($2,151,152 / $3,358,338,602) to 0.87% (86 / 9540) and 0.90% ($37,201,095 / $4,300,145,614). Among active studies, 9 investigators maintain membership in the ARS and serve as principal investigator or project leader in 12 (10%) studies. ARS investigators received the greatest number of awards from the National Institute on Deafness and Other Communication Disrders (n = 8,66.7%), while only receiving 2.2% of awarded funding from the National Institute of Allergy and Infectious Diseases ($607,500/$26,873,022), the largest source of awards for sinusitis research. CONCLUSION: Support for sinusitis research is significantly growing, with the largest source of active funding not being fully utilized by members of the ARS. Further efforts to promote funding priorities among extramural sources is necessary to facilitate increased funding for ARS member initiatives.
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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 | 0.037 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".