Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is a highly prevalent inflammatory disease with significant impacts on patient quality of life and daily productivity. Evaluating the volume of research on CRS, relative to similar chronic diseases, may provide insight into current disparities in research prioritization. METHODS: A systematic review was performed using Ovid MEDLINE (R) (1970 - December 31st, 2014) to define the volume of research publications for CRS, asthma, and diabetes mellitus (DM). Primary outcomes were overall volume of research publications and volume of publications per year. A subgroup analysis was performed using chi-square (χ2) omnibus test with 2×3 contingency tables to identify significant differences in the proportion of total randomized controlled trials, systematic reviews, meta-analyses, and economic evaluation publications between CRS, asthma, and DM groups. RESULTS: There were substantial disparities in the volume of research published over the last 45 years for CRS (n = 7,962), asthma (n = 136,652), and DM (n = 337,411). Although the volume of research for CRS in increasing, the disparities in the annual publication volumes between CRS, asthma, and DM appeared consistent over the last 45 years. CONCLUSIONS: Outcomes from this review have demonstrated a large disparity in the volume of published research for CRS compared to asthma and DM. Given the similarities in prevalence rates, impact on quality of life and economic burden, the relative under supply of CRS research should prompt efforts to increase research prioritization for this chronic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".