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Record W2050193507 · doi:10.1002/lary.20034

The health and resource utilization of Canadians with chronic rhinosinusitis

2008· article· en· W2050193507 on OpenAlexaff
Kristian I. Macdonald, J. Dayre McNally, Emad Massoud

Bibliographic record

VenueThe Laryngoscope · 2008
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of SaskatchewanRoyal University HospitalDalhousie University
Fundersnot available
KeywordsMental healthPopulation healthMedicineDepression (economics)Health careChronic rhinosinusitisPhysical healthPopulationHealth economicsEnvironmental healthPublic healthPsychiatryNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the impact of chronic rhinosinusitis (CRS) on the physical and mental health and health-resource utilization of Canadians. STUDY DESIGN: Cross-sectional. METHODS: Data from the detailed health portion of cycle 3 (1998-1999) of the National Population Health Survey (NPHS), which involved 17,000 Canadians, were used to evaluate Canadians with self-reported CRS. RESULTS: NPHS data confirmed lower mental and physical health, with CRS sufferers being almost three times more likely to report their health as poor (4.6% vs. 1.7%). Health Utility Index data identified a significant decline in the mental health of patients with CRS, which was associated with more depression (8.4% vs. 4.1%), more antidepressant use (9.1% vs. 4.6%), and more visits to mental-health professionals (11.8% vs. 7.0%). CONCLUSIONS: CRS significantly affects both physical and mental health. The mental impact of CRS remains largely unrecognized and should be of greater focus during patient care and in further research.

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 score0.498
Threshold uncertainty score0.836

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 studies0.0010.000
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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

Citations60
Published2008
Admission routes1
Has abstractyes

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