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Record W2595686941 · doi:10.3390/su9030427

The Disease Burden of Patients with Allergic Rhinitis from a Hospital Surveillance in Beijing

2017· article· en· W2595686941 on OpenAlexaff
Fengying Zhang, Chengjing Nie, Li Wang, Mark W. Rosenberg, Jin Xu, Thomas Krafft, Wuyi Wang

Bibliographic record

VenueSustainability · 2017
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineBeijingDisease burdenOtorhinolaryngologyBurden of diseaseOutpatient clinicDiseaseRenminbiEmergency medicinePhysical therapyMedical emergencyPediatricsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: The aims of this study are to estimate the disease burden of allergic rhinitis (AR) patients and examine various underlying issues related to the symptoms and services of adult AR patients. Methods: Beijing hospital was picked as the surveillance area, and self-report questionnaires from the AR patients and data from medical examinations by specialists of otolaryngology were collected. The burden of patients with AR was evaluated by the combined results from patient-questionnaires and specialist examination reports. Results: AR imposed a substantial burden on patients regarding everyday life limitations and work performance; AR affected patients’ noses, ears, throats, and eyes in various ways. The basic daily average medicine cost was 10 RMB for each patient, and the cost for an outpatient in the hospital was 10 RMB for a basic nasal examination and more than 200 RMB if the patient needed further physical examinations. Conclusions: AR imposed burdens on everyday activities and work performance; the patients needed to wait a long time before being diagnosed, and the costs of diagnosis and treatment imposed economic burden on patients.

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)0.002
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 score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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