Models for the study of nasal and sinus physiology in health and disease: A review of the literature
Bibliographic record
Abstract
Objective Chronic sinusitis is a very common yet poorly understood medical condition with significant morbidity. Hence, it remains an entity that is difficult to treat with unsatisfactory outcomes of current management options. This necessitates research into the etiology and pathophysiology of the condition to enhance our knowledge and the therapeutic options. Unfortunately, this kind of research is not always feasible on human subjects due to practical and ethical limitations. Therefore, an alternative model that simulates the disease had to be found in order to overcome these limitations. These models could either be in vivo or in vitro. The aim of our review is to summarize the research findings and key discoveries of both in vivo and in vitro models of chronic sinusitis that have enhanced our understanding of the condition today and have paved the way for the future research of tomorrow. Data Sources: PubMed literature review. Methods A review of the literature was conducted to identify the main successful in vivo and in vitro models for chronic sinusitis. Results Creating a successful model for chronic sinusitis is no easy task. Over the years, both in vivo animal models and in vitro tissue culture models were proposed, with each model having its accolades and pitfalls, with the ideal model remaining elusive to this day. However, advancing three‐dimensional cell culturing techniques seems to be a promising new way to find a more accurate model. Conclusion None of the current models is perfect for a thorough study of chronic sinusitis. However, three‐dimensional cell cultures have the potential to bridge the gap between in vivo and in vitro studies. 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".