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Record W4392705808 · doi:10.7759/cureus.56013

Unveiling the Efficacy of Physiotherapy Strategies in Alleviating Low Back Pain: A Comprehensive Review of Interventions and Outcomes

2024· review· en· W4392705808 on OpenAlexaboutno aff
Priya Tikhile, Deepali S Patil

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyMedicinePsychological interventionLow back painOswestry Disability IndexModalitiesPhysical medicine and rehabilitationBack painManual therapyAlternative medicine

Abstract

fetched live from OpenAlex

Low back pain (LBP) presents a significant burden globally, affecting individuals of all ages, but it is more common in adults aged 30-60 years old and demographics including race, ethnicity, and socioeconomic status. Physiotherapy interventions are commonly employed to manage LBP due to their non-invasive nature and potential for addressing underlying biomechanical dysfunctions. This comprehensive review aims to evaluate the efficacy of various physiotherapy strategies in alleviating LBP, considering a range of interventions and their associated outcomes. Through a thorough examination of existing literature from January 2017 to October 2023, this review synthesises evidence on the effectiveness of interventions such as manual therapy, exercise therapy, electrotherapy modalities, and education-based approaches. The review also scrutinizes the comparative effectiveness of different physiotherapy modalities and their suitability for specific patient populations, considering factors such as chronicity, severity, and underlying pathology. By critically evaluating the evidence base, this review aims to provide insights into the most effective physiotherapy strategies for alleviating LBP, chronic low back pain (CLBP) and chronic nonspecific low back pain (CNLBP) and guiding clinical practice toward evidence-based interventions. The Visual Analogue Scale and Numerical Pain Rating Scale for pain, Oswestry Disability Index and Roland-Morris Disability Questionnaire for disability, Modified-Modified Schober Test for measurement of lumbar flexion and extension and static and dynamic balance for assessing postural stability and balance were among the measures used to foresee enhancements in pain, disability, balance, and LBP symptoms. Twenty-one studies that fulfilled the criteria for inclusion (aged 20 to 50 years and of both genders) were added to the review. Exercises for core stability, strengthening, orthosis (a medical device designed to support, align, stabilise, or correct musculoskeletal structures and functions), transcutaneous electrical nerve stimulation, heat massage therapy, interferential current (a form of electrical stimulation used in physical therapy), Mulligan's mobilization (a manual therapy technique), low-level laser therapy, and McGill stabilization exercises (core exercises) were among the therapeutic strategies. The McKenzie method (back exercises), ultrasound, sensory-motor training, Swiss ball exercises, and other techniques reduced pain and enhanced strength, balance, and ease of daily activities. Every therapeutic approach has an impact on recovery rates ranging from minimal to maximal. Conventional physical therapy is less effective than most recent advanced techniques like mobilisation and exercises. In summary, the integration of manual techniques, orthoses and alternative intervention strategies with conservative therapeutic approaches can effectively alleviate pain, enhance function and yield better overall outcomes. To get more information about the optimal dosage, therapeutic modalities and long-term effects of these treatments, more admirable research is required. This paper aims to expand the scientific discourse by exploring non-traditional physiotherapy interventions and assessing their efficacy in light of the rigorous standards set forth by the latest WHO guidelines.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.202
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.062
GPT teacher head0.437
Teacher spread0.374 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2024
Admission routes1
Has abstractyes

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