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Record W2147213262 · doi:10.1001/archotol.128.6.682

The Role of Woodstoves in the Etiology of Nasal Polyposis

2002· article· en· W2147213262 on OpenAlexaffabout
Julie Kim, James A. Hanley

Bibliographic record

VenueArchives of Otolaryngology - Head and Neck Surgery · 2002
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsHôtel-Dieu de Québec
Fundersnot available
KeywordsEtiologyMedicineIntoxicative inhalantOdds ratioInternal medicineDermatologyEnvironmental healthToxicology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the role of environmental pollutants in the etiology of nasal polyposis. DESIGN: Case-control study. SETTING: A community-based hospital practice in the Gaspesian peninsula in rural northeastern Quebec. PATIENTS: Fifty-five case patients with nasal polyposis and 55 age-matched control subjects without nasal polyposis who were seen at one physician's practice (J.K.) from March 1, 1998, to December 19, 1998. INTERVENTIONS: Exposure to woodstoves, indoor tobacco smoke, and pets and occupational exposures to noxious inhalant compounds. RESULTS: Forty-five (82%) of the cases, but only 14 (25%) of the controls, reported using woodstoves, yielding a crude odds ratio (OR) of 13.1. The corresponding risk associated with occupational exposure to noxious inhalant compounds was also high (OR, 6.1). When adjusted in various ways for the presence of other factors, these ORs remained high and statistically significant. For woodstove use, the point estimates of the ORs were consistently above 10, with the lower limits of 95% confidence intervals above 5. For occupational exposures to noxious inhalant compounds, the various adjusted OR estimates were above 6, with the lower limits above 1.5. CONCLUSIONS: There is a strong association between the use of woodstoves as a principal source of heating and the development of nasal polyposis. Occupational exposures to noxious inhalant compounds (other than tobacco smoke) also play an important role in its etiology.

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 score0.026
Threshold uncertainty score0.214

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 head0.015
GPT teacher head0.248
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

Citations19
Published2002
Admission routes2
Has abstractyes

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