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Pulmonary adaptations to 12 wk of supervised high intensity interval training in COPD: a nonrandomized controlled pilot study

2025· article· en· W4410714230 on OpenAlexfundno aff
Jacob Peter Hartmann, Stine Nymand, Helene Louise Hartmeyer, Amalie Bach Andersen, Milan Mohammad, Cody Durrer, Iben Elmerdahl Rasmussen, Camilla Koch Ryrsø, Rie Skovly Thomsen, Sofie Lindskov Hansen, F. Müller, Michael Perch, Thomas Kromann Lund, Kristine Jensen, Torgny Wilcke, Susan Al-Atabi, Birgitte Hanel, Regitse Højgaard Christensen, Ulrik Winning Iepsen, Jann Mortensen, Ronan M. G. Berg

Bibliographic record

VenueJournal of Applied Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchBeckett-FondenClinical Trials Fund, Canadian Institutes of Health ResearchRigshospitaletTrygFondenP. Carl Petersens FondHelsefonden
KeywordsCOPDMedicineDiffusing capacityInterval trainingHigh-intensity interval trainingCardiologyInternal medicineVO2 maxLung volumesLungPhysical therapyBlood pressureHeart rateLung function

Abstract

fetched live from OpenAlex

Using the combined measurement of the diffusing capacity for carbon monoxide and nitric oxide, we found that the increase in diffusing capacity during submaximal exercise, that is, the alveolar-capillary reserve, was reduced in patients with COPD in a severity-dependent manner. A 12-wk supervised high-intensity training intervention increased exercise capacity but without any changes in alveolar-capillary reserve or lung tissue mass, supporting that the increase in exercise capacity is not caused by pulmonary adaptations.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.310
Teacher spread0.278 · 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 designNon-randomized trial
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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

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