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Record W2121088139 · doi:10.3390/ijerph7114078

Ambient Ozone and Emergency Department Visits for Cellulitis

2010· article· en· W2121088139 on OpenAlexafffundabout
Mieczysław Szyszkowicz, Eugeniusz Porada, Gilaad G. Kaplan, Brian H. Rowe

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2010
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversity of AlbertaUniversity of CalgaryWilfrid Laurier UniversityHealth Canada
FundersCanadian Institutes of Health ResearchHealth CanadaCanada Research ChairsGovernment of Canada
KeywordsInterquartile rangeCellulitisEmergency departmentMedicineConfidence intervalOdds ratioOzoneEmergency medicineInternal medicineSurgeryMeteorology

Abstract

fetched live from OpenAlex

Objectives were to assess and estimate an association between exposure to ground-level ozone and emergency department (ED) visits for cellulitis. All ED visits for cellulitis in Edmonton, Canada, in the period April 1992-March 2002 (N = 69,547) were examined. Case-crossover design was applied to estimate odds ratio (OR, and 95% confidence interval) per one interquartile range (IQR) increase in ozone concentration (IQR = 14.0 ppb). Delay of ED visit relating to exposure was probed using 0- to 5-day exposure lags. For all patients in the all months (January-December) and lags 0 to 2 days, OR = 1.05 (1.02, 1.07). For male patients during the cold months (October-March): OR = 1.05 (1.02, 1.09) for lags 0 and 2 and OR = 1.06 (1.02, 1.10) for lag 3. For female patients in the warm months (April-September): OR = 1.12 (1.06, 1.18) for lags 1 and 2. Cellulitis developing on uncovered (more exposed) skin was analyzed separately, observed effects being stronger. Cellulitis may be associated with exposure to ambient ground level ozone; the exposure may facilitate cellulitis infection and aggravate acute symptoms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.416
Teacher spread0.349 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2010
Admission routes3
Has abstractyes

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