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Record W2076567632 · doi:10.1097/bcr.0000000000000194

Healthcare Costs of Burn Patients From Homes Without Fire Sprinklers

2014· article· en· W2076567632 on OpenAlexafffund
Joanne Banfield, Sarah Rehou, Manuel Gómez, Donald A. Redelmeier, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2014
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsMedicineLibrary scienceHealth careFamily medicineGerontologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The treatment of burn injuries requires high-cost services for healthcare and society. Automatic fire sprinklers are a preventive measure that can decrease fire injuries, deaths, property damage, and environmental toxins. This study's aim was to conduct a cost analysis of patients with burn or inhalation injuries caused by residential fires and to compare this with the cost of implementing residential automatic fire sprinklers. We conducted a cohort analysis of adult burn patients admitted to our provincial burn center (1995-2012). Patient demographics and injury characteristics were collected from medical records and clinical and coroner databases. Resource costs included average cost per day at our intensive care and rehabilitation program, transportation, and property loss. During the study period, there were 1557 residential fire-related deaths province-wide and 1139 patients were admitted to our provincial burn center as a result of a flame injury occurring at home. At our burn center, the average cost was CAN$84,678 per patient with a total cost of CAN$96,448,194. All resources totaled CAN$3,605,775,200. This study shows the considerable healthcare costs of burn patients from homes without fire sprinklers.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.117
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.385
Teacher spread0.339 · 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

Citations20
Published2014
Admission routes2
Has abstractyes

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