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Record W1530386998 · doi:10.1155/2015/376467

Severity of Burn Injury and the Relationship to Socioeconomic Status in Nova Scotia, Canada

2015· article· en· W1530386998 on OpenAlexaffabout
Jeffrey Le, Sarah Al‐Youha, Lihui Liu, Michael Bezuhly, Jason Williams

Bibliographic record

VenueAdvances in Public Health · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNova scotiaQuartileDemographySocioeconomic statusPopulationIncidence (geometry)MedicineGeographyMathematicsInternal medicineSociologyConfidence intervalArchaeology

Abstract

fetched live from OpenAlex

Objective . Few Canadian studies have examined the relationship between socioeconomic status (SES) and incidence of burn injury. We seek to evaluate this relationship using median income as a measure of SES in Nova Scotia, Canada. Methods . Nova Scotia residents admitted to the Queen Elizabeth II burn unit in Halifax, Nova Scotia, from 1995 to 2012, were included in the study. SES was estimated by linking the subject’s postal code to median family household income via Canadian population census data at the level of dissemination areas. Four equal income groups ranging from lowest to highest income quartile were compared (average total burn percentage). Likelihood ratio was calculated to evaluate the effect of median family income burn injury in each income quartile. Results . 302 patients were included in the analysis. Average percent total burn surface area was 19%, 15%, 15%, and 14% (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>0.18</mml:mn></mml:math>) per income quartile (Q1: lowest, Q4: highest), respectively. Likelihood ratios for income quartile Q1–Q4 were 1.3 (0.8–1.6), 1.2 (0.6–1.4), and 0.7 (0.6–1.2), respectively. Conclusion . Contrary to findings in other geographic regions of the world, severity or incidence of burn injury in Nova Scotia, Canada, does not change in relation to SES when using family median income as a surrogate.

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)0.004
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 score0.408
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 head0.054
GPT teacher head0.385
Teacher spread0.331 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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