Systematic review and meta-analysis assessing the effectiveness of local anesthetic, vasoconstrictive, and lubricating agents in flexible fibre-optic nasolaryngoscopy.
Bibliographic record
Abstract
BACKGROUND: Flexible fibre-optic nasolaryngoscopy (FFN) is a common otolaryngology procedure that patients may find uncomfortable. Preparative agents, including topical anesthetic, vasoconstrictive, and lubricating agents, have been studied in randomized controlled trials (RCTs). METHODS: A systematic review and meta-analysis was conducted on RCTs published between January 1966 and October 2005 and indexed to MEDLINE, CINAHL, and Cochrane CENTRAL databases. Methodologic validity was evaluated. Primary outcomes were patients' evaluation of FFN. Secondary outcomes were endoscopists' evaluation of FFN. RESULTS: Eight RCTs were identified studying five preparative agent classes: vasoconstrictors plus topical anesthetics, vasoconstrictors alone, topical anesthetics alone, lubricating agents, saline, and no treatment. The systematic review found no difference in patients' evaluation of pain and discomfort for cocaine versus Co-phenylcaine (two RCTs), Co-phenylcaine versus no active agent (three RCTs), topical anesthetics versus no active preparative agent (two RCTs), vasoconstrictors versus no active preparative agent (two RCTs), and lubricating agents versus nothing. Two RCTs found that Co-phenylcaine causes higher taste unpleasantness, and one RCT found that topical anesthetics cause higher pain. Lubricating agents increase ease of examination but decrease quality of view (one RCT). Meta-analysis of two studies comparing Co-phenylcaine with other preparative agents found no difference on pain scores. CONCLUSIONS: Cumulative evidence from eight RCTs shows no difference in pain scores when preparative agents with vasocontrictive, topical anesthetic, or lubricating properties are used. Co-phenylcaine may cause higher taste unpleasantness, and lidocaine may cause more pain. For the endoscopist, lubricating agents may aid in the examination but compromise the quality of the view.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.040 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".