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Record W2166513998 · doi:10.1016/j.otohns.2004.02.037

Topical Ciprofloxacin/Dexamethasone Otic Suspension is Superior to Ofloxacin Otic Solution in the Treatment of Granulation Tissue in Children with Acute Otitis Media With Otorrhea Through Tympanostomy Tubes

2004· article· en· W2166513998 on OpenAlexaff
Peter S. Roland, Joseph E. Dohar, Brent Lanier, Robert Hekkenburg, Edward M. Lane, Peter J. Conroy, G. Michael Wall, Sheryl J. Dupre, Susan Potts

Bibliographic record

VenueOtolaryngology · 2004
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsBarrie Urology Group
Fundersnot available
KeywordsGranulation tissueMedicineOfloxacinGranulationCiprofloxacinDexamethasoneOtitisAcute otitis mediaAnesthesiaSurgeryDentistryInternal medicineAntibioticsMicrobiologyBiologyWound healing

Abstract

fetched live from OpenAlex

OBJECTIVE: Comparison of topical ciprofloxacin/dexamethasone otic suspension (CIP/DEX) to ofloxacin otic solution (OFL) for treatment of granulation tissue in children with AOMT. STUDY DESIGN: 599 children aged >/=6 months to 12 years with AOMT of up to 3 weeks' duration were enrolled. Patients received either CIP/DEX 4 drops twice daily for 7 days or OFL 5 drops twice daily for 10 days. Granulation tissue severity was graded at clinic visits on days 1, 3, 11, and 18. RESULTS: Granulation tissue was present in 90 of 599 AOMT patients (15.0%) at baseline. CIP/DEX treatment was superior to OFL for reduction of granulation tissue at the day 11 visit (81.3% compared with 56.1%, P = 0.0067) and the day 18 visit (91.7% compared with 73.2%, P = 0.0223). Both topical otic preparations are safe and well tolerated in pediatric patients. CONCLUSION: CIP/DEX was superior to OFL in the treatment of granulation tissue in children with AOMT.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.266
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2004
Admission routes1
Has abstractyes

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