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Record W2606125276 · doi:10.3906/sag-1511-155

An 8-week thoracic spine stabilization exercise program improves postural back pain, spine alignment, postural sway, and core endurance in university students:a randomized controlled study

2017· article· en· W2606125276 on OpenAlexfundaboutno aff
Şeyda Toprak Çelenay, Derya Özer Kaya

Bibliographic record

VenueTURKISH JOURNAL OF MEDICAL SCIENCES · 2017
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersAhi Evran ÜniversitesiMcGill University
KeywordsMedicineCore (optical fiber)Balance (ability)Physical therapyPhysical medicine and rehabilitationTrunkCore stabilityBack painLow back painVisual analogue scaleSpinal manipulation

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: To investigate the effects of an 8-week thoracic stabilization exercise program on back pain, spinal alignment, postural sway, and core endurance in university students. MATERIALS AND METHODS: University students were randomly allocated into exercise (n: 28) and control (n: 25) groups. The exercise program was carried out 3 days a week for 8 weeks. Postural pain, spinal alignment, postural sway, and core endurance were assessed via visual analogue scale, Spinal Mouse, Biodex Balance System, and McGill's trunk muscle endurance tests at the baseline and after 8 weeks of training. RESULTS: Differences were observed for postural pain, thoracic and lumbar curvature, dynamic stability index (eyes closed), and core endurance scores in the exercise group between baseline and week 8 (P < 0.05) and all the parameters were significantly different when compared to those of the control group (P < 0.05). CONCLUSION: The program decreased postural pain, spinal curvatures, and postural sway, and increased core endurance in university students. The program can be effective in postural pain and misalignment of spine problems related to core weakness and balance disorders.

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)0.015
metaresearch head score (Gemma)0.004
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 score0.166
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.376
Teacher spread0.349 · 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

Citations60
Published2017
Admission routes2
Has abstractyes

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