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Record W4236529167 · doi:10.1249/mss.0b013e3182508525

RESPONSE

2012· article· en· W4236529167 on OpenAlexaffabout
Paolo B. Dominelli, Jordan A. Guenette, Sabrina S. Wilkie, Glen E. Foster, A. William Sheel

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntraclass correlationElastic recoilRecoilVital capacityMathematicsMedicineLung functionLungStatisticsPhysicsInternal medicineReproducibilityDiffusing capacity

Abstract

fetched live from OpenAlex

Dear Editor-in-Chief Babb et al. (1) are “…concerned that the calculation of dysanapsis using predicted lung recoil will not accurately reflect true dysanapsis in all individuals.” In our study (2), we divided forced expired flow at 50% of forced vital capacity by the corresponding recoil pressure (Pst(l)50) for each subject. This was done to account for the fact that flow is sensitive to lung recoil as well as airway size. We used previously published regression estimates of Pst(l)50 (3,4). We are, of course, cognizant of the inherent limitations of predictive equations. However, in this case, the usage of predictive values does not appreciably alter the calculation of dysanapsis nor our overall conclusion and interpretation. First, we have measured Pst(l)50 directly using esophageal pressure measures in an identical fashion to that of Mead (3) in 17 young subjects free of any history of smoking. When compared with Mead’s predicted values, our measured values of Pst(l)50 deviated on average by only approximately 1 cm H2O. Furthermore, we found excellent agreement between our measured ratio and the predicted dysanapsis ratio (intraclass correlation (ICC) > 0.8). Second, it would require a large and most likely nonphysiologic difference in Pst(l)50 to have any effect on the subject groupings in our study (2). In order for the non–expiratory flow–limited subjects in our study to have a dysanapsis ratio similar to that of the flow-limited group, their Pst(l)50 would need to be 5 cm H2O higher. Applying a small source of variation (1.39 cm H2O) as suggested by Babb et al. to our values does not affect our classification of subjects. Babb et al. further suggest that it would be worthwhile to probe into the medical history of either lung infections or childhood asthma in these groups. This is, in our view, highly speculative and is an approach that would likely be fraught with methodological problems that would make interpretation difficult if not impossible. For example, most people have had a cold, the flu, or other types of transient pulmonary complication during their life. How would these self-report data be classified and stratified? Moreover, how would the considerable between-subject variation during periods of growth and childhood susceptibility to infections and asthma affect the interrelationship between the airways, the lungs, and the integrated pulmonary response to exercise? We wish to emphasize that Mead’s (3) and our (2) “measures” of airway size are indirect and are, admittedly, not true anatomic measures. Rather, they reflect a functional measure/index of airway size. A promising approach to studying possible male–female differences is to have quantitative anatomic measures of different airway generations using modern imaging methods such as computed tomography or magnetic resonance imaging in subjects for whom detailed lung mechanics data during exercise are also available. Paolo B. Dominelli, MSc Jordan A. Guenette, PhD Sabrina S. Wilkie, MSc Glen E. Foster, PhD A. William Sheel, PhD School of Kinesiology University of British Columbia Vancouver, British Columbia, Canada The authors declare no conflicts of interest.

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)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.2540.157

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 head0.024
GPT teacher head0.331
Teacher spread0.307 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes2
Has abstractyes

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