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Record W2270142572 · doi:10.1136/jramc-2015-000501

A case of a chlorine inhalation injury in an Ebola treatment unit

2015· article· en· W2270142572 on OpenAlexaff
Adrian Carpenter, Andrew T. Cox, D. Marion, Anastasia Phillips, Ian Ewington

Bibliographic record

VenueJournal of the Royal Army Medical Corps · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsMisericordia Community Hospital
Fundersnot available
KeywordsEbola virusSierra leoneMedicineChlorineInhalationRespiratory distressCoronavirus disease 2019 (COVID-19)Chlorine gasIntensive care medicineMedical emergencyEmergency medicineSurgeryDiseaseAnesthesiaInfectious disease (medical specialty)Internal medicineChemistry

Abstract

fetched live from OpenAlex

We present a 26-year-old male British military nurse, deployed to Sierra Leone to treat patients with Ebola virus disease at the military-run Kerry Town Ebola Treatment Unit. Following exposure to chlorine gas during routine maintenance procedures, the patient had an episode of respiratory distress and briefly lost consciousness after exiting the facility. Diagnoses of reactive airways disease, secondary to the chlorine exposure and a hypocapnic syncopal episode were made. The patient was resuscitated with minimal intervention and complete recovery occurred in less than 1 week. This case highlights the issues of using high-strength chlorine solution to disinfect the red zone. Although this patient had a good outcome, this was fortunate. Ensuring Ebola treatment centres are optimally designed and that appropriate management systems are formulated with extraction scenarios rehearsed are important to mitigate the chlorine-associated risk.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.045
GPT teacher head0.312
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 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

Citations22
Published2015
Admission routes1
Has abstractyes

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