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Record W2677840980 · doi:10.7759/cureus.1390

Emergency Management of Tension Pneumothorax for Health Professionals on Remote Cat Island Bahamas

2017· article· en· W2677840980 on OpenAlexaff
Tia Renouf, Michael H. Parsons, Leathe Francis, Cristian Senoro, Caroline Chriswell, Rose Saunders, Charles S. Hollander

Bibliographic record

VenueCureus · 2017
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineTension pneumothoraxHealth professionalsMedical emergencyRural areaHealth careEmergency departmentNursingPneumothoraxSurgeryPathology

Abstract

fetched live from OpenAlex

Patients living in remote areas have higher rates of injury-related death than those living in cities. Rural and remote health professionals working in sparsely populated places, such as Cat Island Bahamas, may have scant resources for treating emergency conditions. Local health professionals must be prepared to rely solely upon clinical judgment to perform emergency "high-stakes low-frequency" procedures while also accurately and effectively communicating with distantly located receiving specialists. However, these health providers may not recently have performed or had the opportunity to practice such emergency procedures. Telesimulation may be a useful way to teach remote practitioners both emergency procedures and communication skills. This technical report describes a simulation exercise for teaching these skills.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.094
GPT teacher head0.477
Teacher spread0.382 · 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 designNot applicable
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

Citations9
Published2017
Admission routes1
Has abstractyes

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