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Record W6986717804

A quantitative investigation into the effectiveness of a 2x2, crossover, randomized control trial, for individuals suffering from non-specific chronic lower back pain, using a physical exercise-focused clinical intervention on the transverse abdominus/ multifidus (stability) and rectus abdominals/ erector spinae (dynamic) muscles.

2024· dissertation· en· W6986717804 on OpenAlexfundaboutno aff

Bibliographic record

VenueResearch Commons (University of Waikato) · 2024
Typedissertation
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersMcGill University
KeywordsCore stabilityTorsoTrunkLow back painCore (optical fiber)Back painVisual analogue scalePsychological interventionBack muscles
DOInot available

Abstract

fetched live from OpenAlex

A plethora of research has been conducted on interventions to reduce non-specific lower back pain and improve the quality of life of sufferers. However, there is a lack of distinction on the complementary nature of these interventions. Our primary research goal was to assess the effectiveness of stability- and dynamic-style clinical interventions, focusing on exercises to strengthen specific trunk muscles, including the transverse abdominis/multifidus (stability-AB), and rectus abdominis/ erector spinae (dynamic). We aimed to determine the effectiveness of these interventions in reducing non-specific chronic lower back pain. Our research questions focused on whether a two-part clinical intervention targeting specific core muscle groups (dynamic or stability) improves pain outcomes, whether exercises targeting specific core muscle groups improve disability index physical impairment and overall low back pain rating scale scores, and if there is a difference between intervention groups and the sequential order of intervention for these outcomes. Additionally, we wanted to investigate the correlation between these outcome measures and trunk endurance strength scores. Seven participants were randomly assigned to either the stability (N =4) or dynamic (N = 3) exercise group. Outcome measures included back and leg pain, lower back pain visual analogue scale scores, disability index, physical impairment, and total Low Back Pain Rating Scale (LBPRS) scores. Four measures of trunk endurance strength were taken using Mcgill's Torso Endurance Test Battery (MTETB), which involves trunk extension, trunk flexor, and trunk lateral tests on both the left and right sides. Assessments were performed before the first clinical intervention, after the first intervention, and after the completion of the final intervention. Each intervention phase lasted 4 weeks for each participant before they switched to the opposing exercise group. The scores of back and leg pain, lower back pain, disability index, and physical impairment all improved significantly after both interventions. We found no difference between the groups during these intervention phases, or any potential crossover/sequential effect, except for the dynamic group showing a significant improvement in physical impairment scores after the first intervention, which remained consistent between interventions one and two. In conclusion, both exercise approaches were similar across all our outcome measures and may serve as complementary interventions for individuals suffering from non-specific chronic low back pain.

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)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.072
GPT teacher head0.383
Teacher spread0.311 · 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 designRandomized trial
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

Citations0
Published2024
Admission routes2
Has abstractyes

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