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Record W2408926233

Effect of regular physical exercise on resting nasal resistance.

2000· article· en· W2408926233 on OpenAlexaff
Michel Bussieres, Louis Përusse, Jacques Leclerc

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsUniversité LavalCegep de Sainte Foy
Fundersnot available
KeywordsMedicineWilcoxon signed-rank testPhysical therapyAcoustic rhinometryNoseInternal medicineSurgeryMann–Whitney U test
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was conducted to determine (1) if long-term regular training changes resting nasal resistance in humans and (2) if the changes are related to the structural component or mucosal component of nasal resistance. METHODS: We used a case-control study to compare a group of 16 athletes to 15 sedentary people of similar age. Nasal resistance was measured by computerized head-out body plethysmograph posterior rhinometry. Physical activity was evaluated by the Baecke questionnaire. RESULTS: The p values (t-test) were very significant for the Baecke sports and total scores (p < .0001) but not for the other variables: age, untreated nasal resistances, decongested nasal resistances, and Baecke work and leisure scores. There were no significant correlations between nasal resistances and indexes of physical activity in all subjects (Pearson's correlation coefficient). The subjects with extremely low and high sports and total scores were paired and studied with the Signed test and the Wilcoxon signed rank test. No significant relationship was found between the nasal resistances and the Baecke scores. CONCLUSIONS: Resting nasal resistances in a group of endurance-trained athletes are identical to those found in a group of sedentary individuals, and this relationship stands for both the structural and mucosal components of nasal resistance. A new study of the same parameters is warranted to follow a cohort of sedentary subjects as they enroll in a physical training program.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.334

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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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 designOther design
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

Citations7
Published2000
Admission routes1
Has abstractyes

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