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Record W4246902325 · doi:10.1111/1556-4029.14235

Elevator‐Related Deaths

2019· article· en· W4246902325 on OpenAlexaff
Joseph A. Prahlow, Zuhha Ashraf, Natalie Plaza, C. D. F. Rogers, Pâmela C.L. Ferreira, David R. Fowler, Melissa M. Blessing, Dwayne A. Wolf, Michael Graham, Kelly Sandberg, Theodore Brown, Patrick E. Lantz

Bibliographic record

VenueJournal of Forensic Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsElevatorAsphyxiaBluntPoison controlForensic engineeringInjury preventionOccupational safety and healthMedicineEngineeringMedical emergencyEnvironmental healthSurgeryPediatrics

Abstract

fetched live from OpenAlex

Elevators are mechanical transportation devices used to move vertically between different levels of a building. When first developed, elevators lacked the safety features. When safety mechanisms were developed, elevators became a common feature of multistory buildings. Despite their well-regarded safety record, elevators are not without the potential for danger of injury or death. Persons at-risk for elevator-related death include maintenance and construction workers, other employees, and those who are prone to risky behavior. Deaths may be related to asphyxia, blunt force, avulsion injuries, and various forms of environmental trauma. In this review, we report on 48 elevator-related deaths that occurred in nine different medicolegal death investigation jurisdictions within the United States over an approximately 30-year period. The data represents a cross-section of the different types of elevator-related deaths that may be encountered. The review also presents an overview of preventive strategies for the purpose of avoiding future elevator-related fatalities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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