Evidence map›Paper›PMID 40502617›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Mapping the Zone of Uncertainty in Pulmonary Function Test Interpretation.

Alexander T Moffett, Meredith C McCormack, Gary E Weissman

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alexander T MoffettDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-8490-4899
Meredith C McCormackDivision of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.ORCID 0000-0003-1702-3201
Gary E WeissmanDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-9588-3819

Funding

Improving sepsis care with AI-based clinical decision supportR35GM155262 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Gary Weissman · 2024 to 2026
$1.2M
Building equitable and feasible clinical decision support tools for population health management of people living with chronic lung diseaseR03HL171424 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI WEISSMAN, GARY · 2024 to 2025
$244k
Applying Causal Inference and Deep Learning to Improve the Accuracy and Equity of Pulmonary Function Test Interpretation.F32HL167456 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MOFFETT, ALEXANDER T · 2023 to 2023
$88k
NHLBI NIH HHS F32 HL167456NHLBI NIH HHS R03 HL171424NIGMS NIH HHS R35 GM155262
6 · The paper itself

Abstract

Background: European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation posit the existence of a "zone of uncertainty." However, as reference equations have been defined only for healthy lung function, the precise borders of the zone of uncertainty remain unspecified. To address this limitation, we sought to define distributions of both healthy and diseased lung function and to use these distributions to map the zone of uncertainty. Methods: We used a latent class model to define distributions of healthy and diseased FEV Results: Pre-bronchodilator spirometry data were collected from 14,075 NHANES participants-of whom 6,063 were without respiratory symptoms or a history of tobacco use-and 41,437 UPHS patients. Healthy lung function was represented by a Box-Cox Cole Green distribution with a median of 0.81, a coefficient of variation of 0.08, and skewness of 1.70. Diseased lung function was best represented by a Box-Cox power exponential distribution with a median of 0.56, a coefficient of variation of 0.30, skewness of 0.91, and kurtosis of 2.36. In the latent class model, the prior probability that an FEV Conclusion: This exploratory study presents evidence that in a clinical cohort, most FEV

Identifiers

PMID40502617
PMCPMC12155011

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.