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Record W4392169212 · doi:10.1080/10903127.2024.2319150

Performance of the Medical Priority Dispatch System® in Identifying Patients Requiring Chest Compressions at Overdose Prevention Services: A Retrospective Cohort Study

2024· article· en· W4392169212 on OpenAlexaff
Richard Armour, Brian Grunau, Sammy Iammarino, Jane A. Buxton, Brooke Kinniburgh, Heather Burgess, Kali-Olt Sedgemore, Paul Choisil, Suzanne Nielsen, Linda Ross

Bibliographic record

VenuePrehospital Emergency Care · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsCentre for Advancing Health OutcomesBC Centre for Disease ControlUniversity of British ColumbiaIsland HealthSt. Paul's HospitalResearch Canada
Fundersnot available
KeywordsMedicineRetrospective cohort studyMedical emergencyEmergency medicineCohortEmergency medical servicesCohort studyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Background and Aims The Medical Priority Dispatch System (MPDS) ® is used to triage 9-1-1 calls according to acuity, with certain coding receiving telecommunicator cardiopulmonary resuscitation (T-CPR) for suspected out-of-hospital cardiac arrest (OHCA). However, this may be challenging for those with drug poisoning emergencies, who may resemble OHCA. We sought to examine the performance of the system to correctly identify cases requiring T-CPR, specifically at overdose prevention services (OPS).Methods This retrospective cohort study included patients attended by the provincial emergency medical system (EMS) (May 1st 2019 – January 31st 2023). We calculated the diagnostic performance of MPDS ® assessment of whether the case required T-CPR instructions against the gold standard of whether the patient was found pulseless on EMS clinician arrival. We compared performance among subgroups, specifically OPS vs. other locations and drug poisoning-classified cases vs. other case classifications.Results Comparing OPS to other locations, the sensitivity of MPDS ® was similar (66.7% vs. 62.4%, p = 0.4), with lower specificity (87.3% vs. 98.1%, p < 0.01) and positive predictive value (0.3% vs. 35.7%, p < 0.01) and higher negative predictive value (99.9% vs. 99.4%, p < 0.01). The negative likelihood ratio of MPDS ® was 0.381 at OPS locations, compared with 0.383 at other locations, while the positive likelihood ratio was 5.24, compared with 32.36. In patients with drug poisoning emergencies, compared with other 9-1-1 events, MPDS ® had higher sensitivity (83.6% vs. 60.6%, p < 0.01) but lower specificity (77.6% vs. 98.9%, p < 0.01) and positive predictive value (10.5% vs. 48.5%, p < 0.01), and similar negative predictive value (99.3% vs. 99.4%, p = 0.03). The negative likelihood ratio of MPDS ® was 0.212 in drug poisoning emergencies compared with 0.398 for all other presentations, and the positive likelihood ratio was 3.73 compared with 57.88.Discussion and Conclusions The ability of MPDS ® to correctly identify patients needing telecommunicator cardiopulmonary resuscitation instructions differed between OPS settings and other locations, frequently recommending T-CPR for patients not suffering OHCA at an OPS. Different strategies developed in collaboration with people who use substances are required to better tailor dispatch instructions prior to EMS arrival to avoid delays in life-saving 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 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)0.002
metaresearch head score (Gemma)0.007
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 score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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