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Nasal Nitric Oxide Measurement in Primary Ciliary Dyskinesia. A Technical Paper on Standardized Testing Protocols

2019· article· en· W2990681887 on OpenAlexaff
Adam J. Shapiro, Sharon Dell, Benjamin Gaston, Michael G. O’Connor, Nadzeya Marozkina, Michele Manion, Milan J. Hazucha, Margaret W. Leigh

Bibliographic record

VenueAnnals of the American Thoracic Society · 2019
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenMcGill University Health CentreUniversity of TorontoMontreal Children's Hospital
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood Institute
KeywordsPrimary ciliary dyskinesiaMedicineNitric oxideOphthalmologyInternal medicineBronchiectasisLung

Abstract

fetched live from OpenAlex

Nasal nitric oxide concentrations are extremely low in primary ciliary dyskinesia (PCD), and measurement of this nasal gas is recommended as a PCD diagnostic test in cooperative patients aged 5 years and older. However, nasal nitric oxide measurements must be performed with chemiluminescence analyzers using a standardized protocol to ensure proper results, because nasal nitric oxide values can be influenced by various internal and external factors. Repeat nasal nitric oxide testing on separate visits is required to ensure that low diagnostic values are persistent and consistent with PCD. This technical paper presents the standard operating procedures for nasal nitric oxide measurement used by the PCD Foundation Clinical and Research Centers Network at various specialty centers across North America. Adherence to this document ensures reliable nasal nitric oxide testing and high diagnostic accuracy when employed in a population with appropriate clinical phenotypes for PCD.

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)0.025
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.024
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.009

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 head0.120
GPT teacher head0.426
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations102
Published2019
Admission routes1
Has abstractyes

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Same venueAnnals of the American Thoracic SocietySame topicCystic Fibrosis Research AdvancesFrench-language works237,207