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Record W2109284757 · doi:10.1017/s1049023x00008244

Does Emergency Medical Dispatch Priority Predict Delphi Process-Derived Levels of Prehospital Intervention?

2010· article· en· W2109284757 on OpenAlexaff
Karl A. Sporer, Alan Craig, Nicholas J. Johnson, Clement Yeh

Bibliographic record

VenuePrehospital and Disaster Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsAssociated Medical Services
Fundersnot available
KeywordsPsychological interventionMedicineEmergency medical servicesAdvanced life supportDelphi methodIntervention (counseling)Medical emergencyBasic life supportEmergency medicineComputer scienceCardiopulmonary resuscitationPsychiatryResuscitation

Abstract

fetched live from OpenAlex

OBJECTIVE: The Medical Priority Dispatch System (MPDS) is an emergency medical dispatch system widely used to prioritize 9-1-1 calls and optimize resource allocation. This study evaluates whether the assigned priority predicts a Delphi process-derived level of prehospital intervention in each emergency medical dispatch category. METHODS: All patients given a MPDS priority in a suburban California county from 2004-2006 were included. A Delphi process of emergency medical services (EMS) professionals in another system developed the following categories of prehospital treatment representing increasing acuity, which were adapted for this study: advanced life support (ALS) intervention, ALS-Stat, and ALS-Critical. The sensitivities and specificities of MPDS priority for level of prehospital intervention were determined for each MPDS category. Likelihood ratios of low and high priority dispatch codes for the level of prehospital intervention also were calculated for each MPDS category. RESULTS: A total of 65,268 patients met inclusion criteria, representing 61% of EMS calls during the study period. The overall sensitivities of high-priority dispatch codes for ALS, ALS-Stat, and ALS-Critical interventions were 83% (95% confidence interval 83-84%), 83% (82-84%), and 94% (92-96%). Overall specificities were: ALS, 32% (31-32%); ALS-Stat, 31% (30-31%); and ALS-Critical 28% (28-29%). Compared to calls assigned to a low priority, calls with high-priority dispatch codes were more likely to receive ALS interventions by 22%, ALS-Stat by 20%, and ALS-Critical by 32%. A low priority dispatch code decreased the likelihood of ALS interventions by 48%, ALS-Stat by 45%, and ALS-Critical by 80%. Among high-priority dispatch codes, the rates of interventions were: ALS 26%, ALS-Stat 22%, and ALS-Critical 1.5%, all of which were significantly greater than low-priority calls (p<0.05) [ALS 13%, ALS-Stat 11%, and ALS-Critical 0.2%]. Major MPDS were categories with high sensitivities (>95%) for ALS interventions included breathing problems, cardiac or respiratory arrest/death, chest pain, stroke, and unconscious/fainting; these categories had an average specificity of 3%. Medical Priority Dispatch System categories such as back pain, unknown problem, and traumatic injury had sensitivities for ALS interventions<15%. CONCLUSIONS: The MPDS is moderately sensitive for the Delphi process derived ALS, ALS-Stat, and ALS-Critical intervention levels, but nonspecific. A low MPDS priority is predictive of no prehospital intervention. A high priority, however, is of little predictive value for ALS, ALS-Stat, or ALS-Critical interventions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.309
Teacher spread0.297 · 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.

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

Citations29
Published2010
Admission routes1
Has abstractyes

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