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Record W4327951943 · doi:10.3390/sports11030071

Effects of Malocclusion on Maximal Aerobic Capacity and Athletic Performance in Young Sub-Elite Athletes

2023· article· en· W4327951943 on OpenAlexaff
El Mokhtar El Ouali, Hassane Zouhal, Loubna Bahije, Azeddine Ibrahimi, Bahae Benamar, Jihan Kartibou, Ayoub Saeidi, Ismail Laher, Sanae El Harane, Urs Granacher, Abdelhalem Mesfioui

Bibliographic record

VenueSports · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Physical Performance
Canadian institutionsUniversity of British Columbia
FundersDeutsche Forschungsgemeinschaft
KeywordsAthletesElite athletesEliteMalocclusionPhysical therapyMedicinePhysical medicine and rehabilitationOrthodonticsPolitical science

Abstract

fetched live from OpenAlex

Oral pathologies can cause athletic underperformance. The aim of this study was to determine the effect of malocclusion on maximal aerobic capacity in young athletes with the same anthropometric data, diet, training mode, and intensity from the same athletics training center. Sub-elite track and field athletes (middle-distance runners) with malocclusion (experimental group (EG); n = 37; 21 girls; age: 15.1 ± 1.5 years) and without malocclusion (control group (CG); n = 13; 5 girls; age: 14.7 ± 1.9 years) volunteered to participate in this study. Participants received an oral diagnosis to examine malocclusion, which was defined as an overlapping of teeth that resulted in impaired contact between the teeth of the mandible and the teeth of the upper jaw. Maximal aerobic capacity was assessed using the VAMEVAL test (calculated MAS and estimated VO2max). The test consisted of baseline values that included the following parameters: maximum aerobic speed (MAS), maximal oxygen uptake (VO2max), heart rate frequency, systolic (SAP) and diastolic arterial pressure (DAP), blood lactate concentration (LBP), and post-exercise blood lactate assessment (LAP) after the performance of the VAMEVAL test. There were no statistically significant differences between the two study groups related to either anthropometric data (age: EG = 15.1 ± 1.5 vs. CC = 14.7 ± 1.9 years (p = 0.46); BMI: EG = 19.25 ± 1.9 vs. CC = 19.42 ± 1.7 kg/m2 (p = 0.76)) or for the following physical fitness parameters and biomarkers: MAS: EG = 15.5 (14.5–16.5) vs. CG = 15.5 (15–17) km/h (p = 0.47); VO2max: EG = 54.2 (52.5–58.6) vs. CG = 54.2 (53.4–59.5) mL/kg/min (p = 0.62) (IQR (Q1–Q3)); heart rate before the physical test: EG = 77.1 ± 9.9 vs. CG = 74.3 ± 14.0 bpm (p = 0.43); SAP: EG = 106.6 ± 13.4 vs. CG = 106.2 ± 14.8 mmHg (p = 0.91); DAP: EG = 66.7 ± 9.1 vs. CG = 63.9 ± 10.2 mmHg (p = 0.36); LBP: EG = 1.5 ± 0.4 vs. CG = 1.3 ± 0.4 mmol/L (p = 0.12); and LAP: EG = 4.5 ± 2.36 vs. CG = 4.06 ± 3.04 mmol/L (p = 0.60). Our study suggests that dental malocclusion does not impede maximal aerobic capacity and the athletic performance of young track and field athletes.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.195
Teacher spread0.190 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

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