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Record W2107210012 · doi:10.3109/17482968.2012.720262

The effects of lung volume recruitment on coughing and pulmonary function in patients with ALS

2012· article· en· W2107210012 on OpenAlexafffund
Stuart Cleary, John E. Misiaszek, Sanjay Kalra, Sonya Wheeler, Wendy Johnston

Bibliographic record

VenueAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration · 2012
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMisericordia Community HospitalCovenant HealthUniversity of Alberta
FundersUniversity of Alberta
KeywordsMedicineVital capacityPulmonary function testingLung volumesAmyotrophic lateral sclerosisLung functionAnesthesiaLungInternal medicineDiffusing capacityDisease

Abstract

fetched live from OpenAlex

Our objective was to study the intensity and duration of the effects of lung volume recruitment, a manual breath stacking technique, on pulmonary function and coughing in individuals with amyotrophic lateral sclerosis (ALS). Twenty-nine individuals with ALS participated in this study. A cross-over research design was used to compare effects of lung volume recruitment to a control condition. Treatment outcome measures included forced vital capacity (FVC), sniff nasal pressure (SnP) and peak cough flow (PCF). Results demonstrated that LVR had a significantly positive effect on FVC for up to 15 min following treatment but did not have a facilitative effect on SnP at any time-point. LVR had a significantly positive effect on PCF during unassisted coughing at both 15 min and 30 min following treatment, and there was no significant decrease in flow rates from baseline to 30 min later. In conclusion, lung volume recruitment may be an effective treatment for improving coughing and pulmonary function in individuals with ALS. Future research should be focused on determining patient characteristics that contribute to response to treatment, as well as randomized controlled trials of the technique.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.244
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations44
Published2012
Admission routes2
Has abstractyes

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