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Record W2981328317 · doi:10.1016/j.eclinm.2019.09.013

Are respiratory rate counters really so bad? Throwing the baby out with the bath water

2019· article· en· W2981328317 on OpenAlexaff
J. Mark Ansermino, Dustin Dunsmuir, Walter Karlen, Heng Gan, Guy A. Dumont

Bibliographic record

VenueEClinicalMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineRespiratory rateConcordanceCapnographyPediatricsHeart rateInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

We congratulate Baker et al. [[1]Baker K Alfvén T Mucunguzi A Wharton-smith A Dantzer E Habte T et al.Performance of four respiratory rate counters to support community health workers to detect the symptoms of pneumonia in children in low resource settings: a prospective, multicentre, hospital-based, single-blinded, comparative trial.EClinicalMedicine. 2019; (preprint)https://doi.org/10.1016/j.eclinm.2019.05.013Summary Full Text Full Text PDF PubMed Scopus (13) Google Scholar] on their bold attempt to evaluate respiratory rate counters. However, their data show significant variability and wide limits of agreement with all devices which are much greater than reported in previous studies. The gross errors (>30 breaths/ minute) are much more likely due to artifacts in the reference capnometer device or the lack of breath identification by the observer than test device performance. We strongly support the use of capnography as a reference device for respiratory rate measurement. However, this invasive procedure introduces many additional risks. The difficulty of using capnography in awake children is reflected in the fact that one quarter of observations were withdrawn [[1]Baker K Alfvén T Mucunguzi A Wharton-smith A Dantzer E Habte T et al.Performance of four respiratory rate counters to support community health workers to detect the symptoms of pneumonia in children in low resource settings: a prospective, multicentre, hospital-based, single-blinded, comparative trial.EClinicalMedicine. 2019; (preprint)https://doi.org/10.1016/j.eclinm.2019.05.013Summary Full Text Full Text PDF PubMed Scopus (13) Google Scholar]. Capnometers measure respiratory rate by detecting the presence of exhaled carbon dioxide (CO2) in each breath [[2]Eipe N Doherty DR. A review of pediatric capnography.J Clin Monit Comput. 2010; 24 (Aug): 261-268Crossref PubMed Scopus (20) Google Scholar]. The magnitude, regularity and shape of the CO2 waveform must be used to confirm the rate [[3]Krauss B Hess DR. Capnography for procedural sedation and analgesia in the emergency department.Ann Emerg Med. 2007; 50: 272-281Summary Full Text Full Text PDF PubMed Scopus (126) Google Scholar]. This is especially important in small children with rapid breathing rates and small tidal volumes which result in dilution of the end tidal gas. Expert observer counting and analysis of sequential observations from each observer should help in identifying the cause of these gross errors. The inability to identify a breath should be considered less a failure of the device and more of the observer. The clinical measurement of respiratory rate is widely used in clinical diagnosis in children. Until the performance of automated counters have been established, the use of respiratory rate counters should not be discarded based on this study alone. The authors are the inventors of the RRate app. that has been evaluated by Baker et al. Performance of Four Respiratory Rate Counters to Support Community Health Workers to Detect the Symptoms of Pneumonia in Children in Low Resource Settings: A Prospective, Multicentre, Hospital-Based, Single-Blinded, Comparative TrialNone of the four devices evaluated performed well based on agreement with the reference standard. The ARI timer currently recommended for use by CHWs should only be replaced by more expensive, equally performing, automated RR devices when aspects such as usability and duration of the device significantly improve the patient-provider experience. Full-Text PDF Open AccessMore work needs to be done to ensure that better pneumonia diagnostics aids are developed and launched to better support frontline health workers–A response to “Are respiratory rate counters really so bad” by Ansermino et al.We thank Ansermino et al for their valuable contribution to the discussion on evaluating respiratory rate (RR) diagnostic aids. We completely agree when they say “Until the performance of automated counters have been established, the use of respiratory rate counters should not be discarded based on this study alone”. While we did not test automated RR diagnostic aids in our study [1], we did see large variations in the agreement between the four RR diagnostic aids we tested and the reference standard presented, i.e the Masimo Root patient monitoring and connectivity platform with Phasein ISA CO2 capnography using nasal cannulas. Full-Text PDF Open Access

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.073
GPT teacher head0.392
Teacher spread0.319 · 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; both teacher heads agree on what is shown here.

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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