MétaCan
Menu
Back to cohort
Record W4385344441 · doi:10.3390/healthcare11152145

Shoulder Pain and Trunk Muscles Endurance in Young Male and Female Swimmers

2023· article· en· W4385344441 on OpenAlexaboutno aff
Nikolaos Paramanidis, Athanasios Kabasakalis, Νικόλαος Κουτλιάνος, George Tsalis, Evangelia Kouidi

Bibliographic record

VenueHealthcare · 2023
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkPhysical medicine and rehabilitationMedicinePhysical therapyPsychologyBiology

Abstract

fetched live from OpenAlex

Shoulder pain is a common syndrome in swimming and affects a large number of competitive swimmers. The purpose of the study was to investigate the relationship between pain in the shoulder girdle and the endurance of the trunk muscles in young swimmers. A total of 24 boys and 22 girls, aged 13 to 18 years, participated in the study. The measurements included the completion of a questionnaire (Shoulder Pain and Disability Index, SPADI) and a field test (McGill’s Torso Muscular Endurance Test). The total SPADI score correlated weakly and negatively with the endurance time of back muscles in both sexes (r2 = 0.10, p = 0.035), and moderately and negatively in girls (r2 = 0.23, p = 0.023). A weak negative correlation was found between the disability index and the back muscles’ endurance time in both sexes (r2 = 0.15, p = 0.007), which was moderate in girls only (r2 = 0.25, p = 0.019). The disability index displayed moderate negative correlations with the right oblique’s (r2 = 0.18, p = 0.049) and left oblique’s endurance time (r2 = 0.23, p = 0.024) in girls. Weight, body mass index, the total out-of-water training time per week and age significantly affected the endurance times of the trunk muscles in boys and girls (p < 0.05). In conclusion, strengthening the dorsal and the oblique muscles could reduce shoulder pain and disability in young swimmers and especially girls.

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 score0.058
Threshold uncertainty score0.415

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 head0.052
GPT teacher head0.354
Teacher spread0.301 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHealthcareSame topicShoulder Injury and TreatmentFrench-language works237,207