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Record W2065505334 · doi:10.1038/npjpcrm.2015.8

Differences in spirometry interpretation algorithms: influence on decision making among primary-care physicians

2015· article· en· W2065505334 on OpenAlexafffundabout
Xiao-Ou He, Anthony D’Urzo, Pieter Jugovic, Reuven Jhirad, Prateek Sehgal, Evan J. Lilly

Bibliographic record

Venuenpj Primary Care Respiratory Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsSpirometryMedicineAsthmaBronchodilatorCOPDVital capacityPrimary careAlgorithmPhysical therapyFamily medicineInternal medicineLungLung functionMathematics

Abstract

fetched live from OpenAlex

Spirometry is recommended for the diagnosis of asthma and chronic obstructive pulmonary disease (COPD) in international guidelines and may be useful for distinguishing asthma from COPD. Numerous spirometry interpretation algorithms (SIAs) are described in the literature, but no studies highlight how different SIAs may influence the interpretation of the same spirometric data. We examined how two different SIAs may influence decision making among primary-care physicians. Data for this initiative were gathered from 113 primary-care physicians attending accredited workshops in Canada between 2011 and 2013. Physicians were asked to interpret nine spirograms presented twice in random sequence using two different SIAs and touch pad technology for anonymous data recording. We observed differences in the interpretation of spirograms using two different SIAs. When the pre-bronchodilator FEV1/FVC (forced expiratory volume in one second/forced vital capacity) ratio was >0.70, algorithm 1 led to a ‘normal’ interpretation (78% of physicians), whereas algorithm 2 prompted a bronchodilator challenge revealing changes in FEV1 that were consistent with asthma, an interpretation selected by 94% of physicians. When the FEV1/FVC ratio was <0.70 after bronchodilator challenge but FEV1 increased >12% and 200 ml, 76% suspected asthma and 10% suspected COPD using algorithm 1, whereas 74% suspected asthma versus COPD using algorithm 2 across five separate cases. The absence of a post-bronchodilator FEV1/FVC decision node in algorithm 1 did not permit consideration of possible COPD. This study suggests that differences in SIAs may influence decision making and lead clinicians to interpret the same spirometry data differently. Variations among algorithms used to interpret ‘blow’ tests for diagnosis of asthma and lung disease may be skewing test results. The researchers, led by Anthony D'Urzo from the University of Toronto in Canada, had 113 primary care physicians make diagnoses from nine sets of blow test or spirogram results using two different spirogram interpretation algorithms (SIAs). They found for a particular case with impaired blow test results, one SIA resulted in a ‘normal’ diagnosis by 78% of physicians, while the other resulted in a diagnosis of ‘consistent with asthma’ by 94% of doctors. The findings suggest a need to standardise the algorithms in order to minimise differences in interpreting data, and underscore the importance of educating physicians about the pitfalls of using spirograms in isolation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.325
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

Citations9
Published2015
Admission routes3
Has abstractyes

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