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Record W2914770591 · doi:10.1177/0840470418810678

Clinical leadership in reducing risk: Managing patient airways

2019· article· en· W2914770591 on OpenAlexaff
N. Shira Brown, John Chirico, Melanie Hollidge, Jill Randall

Bibliographic record

VenueHealthcare Management Forum · 2019
Typearticle
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsRegional Municipality of NiagaraNiagara Health SystemMcMaster University
Fundersnot available
KeywordsAirwayAirway managementBusinessCritical pathwaysRisk managementMedicineOperations managementMedical emergencyRisk analysis (engineering)Process managementIntensive care medicineSurgeryEngineeringFinance

Abstract

fetched live from OpenAlex

Niagara Health, a multi-site hospital organization, has developed a multimodal, comprehensive strategy to manage patients with a Difficult Airway (DA) in a non-operative setting. The Difficult Airway Pathway (DAP) is an evidence-based strategy aimed to train staff to reduce critical events. The DAP initiative aligns with the LEADS framework for change management and includes an annual review of reported critical incidents and an Enterprise Risk Management (ERM) Assessment Summary, with the goal to "create a regional systematic approach to support personnel, equipment and education." The guiding vision is: "Right people, Right equipment, Right timing: No failed airway." Preliminary evaluation suggests the strategy reduces morbidity and mortality of difficulty airway incidents outside the operating room.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.662
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.079
GPT teacher head0.338
Teacher spread0.259 · 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 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

Citations4
Published2019
Admission routes1
Has abstractyes

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