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Record W2322853152 · doi:10.2500/ajra.2010.24.3487

Selective Irrigation of Paranasal Sinuses in the Treatment of Recalcitrant Chronic Sinusitis

2010· article· en· W2322853152 on OpenAlexaff
Ali Moshaver, Louis Velazquez-Villasenor, François Lavigne, Ian Witterick

Bibliographic record

VenueAmerican Journal of Rhinology and Allergy · 2010
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsHôpital Notre-DameUniversity of Toronto
Fundersnot available
KeywordsMedicineSinusitisParanasal sinusesChronic rhinosinusitisAntibioticsProspective cohort studySinus (botany)Maxillary sinusSurgeryNasal LavageChronic sinusitisClinical trialNoseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic rhinosinusitis (CRS) refractory to medical and surgical therapy is a challenging entity to treat. Topical antibiotics and anti-inflammatory drugs have been increasingly used in managing this disorder. The aim of this pilot study is to evaluate the role of maxillary sinus antrostomy tubes (MAST) in selectively irrigating paranasal sinuses with topical antibiotics with anti-inflammatory in treating recalcitrant CRS. A prospective clinical trial was performed at a tertiary referral center. METHODS: Thirteen patients with failed maximal medical and surgical therapies for chronic sinusitis were enrolled in the study. Endoscopic scores as well as the Sino-Nasal Outcome Test 16 (SNOT-16) scores were obtained before and 3, 8, and 16 weeks after maxillary sinus intubation with MAST. All patients received topical antibiotics with anti-inflammatory medication for 21 days. RESULTS: Statistically significant reductions in SNOT-16 and endoscopic scores were found before and after topical irrigations. Both scores continued to improve at the 8th and 16th weeks. CONCLUSION: Selective irrigation of maxillary sinuses using MAST is a viable alternative in treating recalcitrant CRS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2010
Admission routes1
Has abstractyes

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