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Record W2033102235 · doi:10.1155/2013/720879

Factors Associated with the Use of Different Treatment Modalities among Patients with Upper Airway Diseases in Taiwan: A Cross-Sectional Survey Study

2013· article· en· W2033102235 on OpenAlexaff
Malcolm Koo, Kai‐Li Liang, Hsin Tsao, Ting‐Ting Yen, Rong‐San Jiang, Yueh‐Chiao Yeh

Bibliographic record

VenueJournal of Allergy · 2013
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCross-sectional studyOtorhinolaryngologyLogistic regressionEtiologyInternal medicineQuality of life (healthcare)AirwayModalitiesSurgeryPathology

Abstract

fetched live from OpenAlex

Rhinitis is a common upper airway disease and can have great impact on patients' quality of life. Factors associated with the use of common treatment modalities among 279 Taiwanese rhinitis patients from the outpatient department of otolaryngology in a medical center were investigated using a cross-sectional survey study. Results from multiple logistic regression analysis, adjusted for etiologies of rhinitis, revealed that males were associated with surgical intervention (OR = 2.11, P = 0.009). Lower educational level was associated with oral (OR = 2.31, P = 0.024) and topical medications (OR = 2.50, P = 0.005). Poor or fair general health status was associated with topical medications (OR = 4.47, P = 0.001), whereas very good or excellent general health status was inversely associated with surgical intervention (OR = 0.32, P = 0.002). Smoking was associated with the use of nasal irrigation (OR = 2.72, P = 0.003). Worse disease-specific quality of life was associated with oral medications (OR = 2.46, P = 0.010) and traditional Chinese medicine (OR = 5.43, P < 0.001). In conclusion, the use of different treatment modalities for rhinitis was associated with different combinations of independent factors.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.283

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 studies0.0000.000
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 head0.053
GPT teacher head0.261
Teacher spread0.208 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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