Distribution of topical agents to the paranasal sinuses: an evidence‐based review with recommendations
Bibliographic record
Abstract
BACKGROUND: The objective of this work was to review the literature concerning the distribution of topical therapeutics to the sinuses versus nasal cavity regarding: surgical state, delivery device, head position, and nasal anatomy and to provide evidence-based recommendations. METHODS: A systematic review was conducted using Medline, EMBASE, and Cochrane databases to perform a Medical Subject Heading search of the literature from 1946 until the last week of May 2012. Articles were independently reviewed and graded for level of evidence. All authors came to consensus on recommendations through an iterative process. RESULTS: Recommendations were made for: improved sinus delivery with high-volume devices and after standard sinus surgery. Recommendations were made against low-volume delivery devices, such as drops, sprays, or simple nebulizers as they do not reliably reach the sinuses. If large-volume devices are not tolerated, low-volume devices are recommended using the lying head back or lateral head low positions to improve nasal cavity distribution to the middle meatus or olfactory cleft. CONCLUSION: Surgery, volume of device, head position, and nasal anatomy were shown to impact distribution to the sinuses. Recommendations are made based upon this evidence as to how to best maximize therapeutic distribution to the sinuses.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".