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Record W4389159519 · doi:10.1164/rccm.202310-1761le

Use FEV1/FVC <i>Z</i> -Score Staging to Minimize Sex and Age Bias in Staging Chronic Obstructive Pulmonary Disease

2023· letter· en· W4389159519 on OpenAlexaff
Brian L. Graham

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2023
Typeletter
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePulmonary diseaseInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

chronic obstructive pulmonary disease (COPD): STaging of Airflow obstruction by Ratio (STAR), an FEV 1 /FVC-based stratification approach (using FEV 1 /FVC thresholds of >0.60 to ,0.70, >0.50 to ,0.60, >0.40 to ,0.50, and ,0.40, respectively, for stages 1-4).The predictive performance for 10-year mortality of STAR grades was comparable with that of the conventional FEV 1 % predicted (ppFEV 1 )-based stratification, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades (2).Moreover, STAR showed a more uniform gradation of disease severity, as it provided better ability to discriminate survival between mild COPD (stage 1) and non-COPD compared with the GOLD stages.Recently, several studies have shown the clinical importance of preserved ratio impaired spirometry (PRISm; FEV 1 /FVC > 0.70 and ppFEV 1 , 0.80), especially for its high incidence of COPD and poor prognosis (3, 4).Nonetheless, subjects with PRISm may be often overlooked, as it does not meet the conventional criterion for COPD (FEV 1 /FVC , 0.70) (2).Given the rising interest in the epidemiologic issues of PRISm, physicians today may have to investigate the implications of impaired ppFEV 1 , including PRISm, together with COPD in clinical research.In the study by Bhatt and colleagues (1), non-COPD was defined as FEV 1 /FVC > 0.70 regardless the value of ppFEV 1 , which is in line with GOLD standards; subjects with PRISm were included in the non-COPD group.According to Figure 1 in Bhatt and colleagues' paper, ppFEV 1 in subjects without COPD was distributed unfavorably to that in GOLD stage 1 subjects, whereas ppFEV 1 was higher in subjects without COPD than in STAR stage 1.As a decrease in ppFEV 1 has been known to be a strong risk factor for COPD morbidity and mortality (5, 6), the discrepancy in overall survival between STAR stage 1 and GOLD stage 1 was considered sensible to the difference in ppFEV 1 between them.Therefore, to assess the impact of ppFEV 1 on all-cause mortality, it is crucial to stratify the entire cohort regardless of the values of FEV 1 /FVC (not only subjects with COPD) according to ppFEV 1 (i.e., ppFEV 1 thresholds of >0.80, >0.50 to ,0.80, >0.30 to ,0.50, and ,0.30) and to assess the mortality of each subgroup.In addition, a comparison of prognostic performance between ppFEV 1 and FEV 1 /FVC among all subjects might make physicians reconsider not only the severity grading but also the diagnostic criteria for COPD, as the cutoff points for the diagnosis of other major noncommunicable diseases (e.g., hypertension, diabetes, dyslipidemia) were established on the evidence of morbidity and mortality.In conclusion, the work by Bhatt and colleagues (1) is intriguing, in that FEV 1 /FVC-a simply calculable biomarker without age-, gender-, height-, and race-dependent predicted values-could be helpful in evaluating disease severity as well as diagnosing COPD.We believe that this new STAR can create a STIR in clinical practice and the management of COPD.

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)0.018
metaresearch head score (Gemma)0.028
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: Editorial · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.057
GPT teacher head0.340
Teacher spread0.284 · 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
GenreEditorial

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

Citations2
Published2023
Admission routes1
Has abstractyes

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