A blinded <i>in‐vitro</i> study to compare the efficacy of five topical ear drops in clearing grommets blocked with thick middle ear effusion fluid<sup>1</sup>
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of 5% NaHCO3, 3% H2O2, Sofradex (dexamethasone sodium metasulphobenzoate 0.05%, framycetin sulphate 0.5%, gramicidin 0.005%), 0.33% acetic acid and 0.9% NaCl eardrops in clearing grommets blocked with harvested thick middle ear effusion fluid. STUDY DESIGN: A blinded in-vitro study. SETTING: District general hospital. PARTICIPANTS: A total of 473 grommets were blocked with freshly harvested unpooled thick middle ear effusion fluid obtained from 68 patients. MAIN OUTCOME MEASURES: Patency of the grommets before and 7 days after intervention was ascertained by tympanometry and close visual inspection. RESULTS: Instillation of eardrops leads to a statistically significant increase in the clearance of grommets as compared with not using any drops (chi2 = 14.3, d.f. = 5, P = 0.006). The numbers needed to treat were 2.8 for NaHCO3, 3.2 for 0.9% NaCl, 3.9 for 0.33% acetic Acid, 4.4 for Sofradex and 9.5 for H2O2 eardrops. Pair-wise comparison was only significant for comparison between 5% NaHCO3 and 3% H2O2 eardrops (Bonferroni corrected P = 0.01, odds ratio = 4.3, CI = 1.9-9.9). CONCLUSIONS: Use of eardrops leads to a clinically and statistically significant increase in the clearance of blocked grommets. Of the five drops used, 5% NaHCO3 was the most efficacious and 3% H2O2 the least efficacious. Limitations of this in-vitro study are recognized and a prospective in-vivo double blind randomized controlled trial is planned.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".