MétaCan
Menu
Back to cohort
Record W4410445777 · doi:10.1186/s13049-025-01405-3

TeLePhone Respiratory (TeLePoR) score to assess the risk of immediate respiratory support through phone call for acute dyspnoea: a prospective cohort study

2025· article· en· W4410445777 on OpenAlexaff
Frédéric Balen, François Saget, Axel Benhamed, Oussama-Ibrahim Boudjemline, Lisa Girard, Paul Reuter, Sandrine Charpentier, Nicolas Marjanovic

Bibliographic record

VenueScandinavian Journal of Trauma Resuscitation and Emergency Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsMedicineEmergency medicineProspective cohort studyRespiratory systemCohort studyCohortIntensive care medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Acute dyspnea is a frequent cause to call the Emergency Medical Call Center (EMCC). The main challenge for EMCC dispatchers is to quickly identify patients that will require respiratory support in order to provide them with the most accurate prehospital response. Our main objective was to derivate a score assessable during the first call to detect the most severe patients needing medical assistance. METHODS: This prospective observational cohort study was conducted in four different French EMCC from January 22nd to March 7th 2024. Patients over the age of 18 years old that called once the EMCC for acute dyspnea were included in our study. The primary endpoint was an immediate respiratory support requirement (i.e. high-flow oxygen, non-invasive ventilation or mechanical ventilation after intubation) before or at the Emergency Department Registration. Variables of interest to predict respiratory support were prospectively collected in each EMCC. A multivariate analysis by stepwise logistic regression was used to select variables associated with the primary endpoint and to create in the TeLePhon Respiratory Score (TeLePoR score). The TeLePoR score was compared to medical dispatcher intuition for predicting respiratory support. RESULTS: Six hundred and forty-nine patients were analyzed, including 49 (8%) that required immediate respiratory support. The risk factors included in the TeLePoR score were: altered ability to speak complete sentences (OR = 8.62; CI95% = [3.49-21.3]), abdominal respiration (OR = 2.42; CI95% = [1.23-4.76]), altered consciousness (OR = 2.05; CI95% = [0.90-4.65]) and self-report breathing discomfort > 7/10 (OR = 1.83; CI95% = [0.96-3.47]) respectively. Considering these factors, TeLePoR score presented a 0.810 AUC. Medical dispatcher intuition was not statistically superior to TelePoR score to predict immediate respiratory support (AUC = 0.836 vs. 0.810; p = 0.431). CONCLUSION: TeLePoR score is a simple scoring system including 4 variables to predict immediate respiratory support in patients calling the EMCC for acute dyspnea.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.374
Teacher spread0.318 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueScandinavian Journal of Trauma Resuscitation and Emergency MedicineSame topicRespiratory Support and MechanismsFrench-language works237,207