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Record W2256283876 · doi:10.1177/0971097320120105

Profile of Deaths due to Electrocution: A Retrospective Study

2012· article· en· W2256283876 on OpenAlexaboutno aff
B. D. Gupta, Rahul A. Mehta, Mahesh M. Trangadia

Bibliographic record

VenueJournal of Indian Academy of Forensic Medicine · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsElectrocutionAccidentalMedicineDemographyPopulationRetrospective cohort studyMedical emergencyEnvironmental healthVeterinary medicineForensic engineeringSurgeryEngineeringSociology

Abstract

fetched live from OpenAlex

We carried out a retrospective analysis of deaths due to electrocution from the medico-legal deaths reported to our institution.Majority of the victims were males belonging to the age group of 11-50 years.Almost all deaths were accidental and most of them were concentrated in the period of monsoon implicating the important role of wetness in causing these deaths.In contrast to the studies done in the West, bathtubs, heaters or hair dryers were not involved in any of the deaths.The mortality rate due to electrocution was significantly higher at 4.4 per one lakhs (100000) population in the present study as against the figures of 0.94 and 0.14 from Bulgaria and Canada respectively.Most of the deaths were either instantaneous or immediate and most of the deaths were preventable by electrocution.It signifies that people living at home did not have elementary knowledge of risks of electrocution; therefore awareness about use of good quality electric appliances and cables is the need of the hour.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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