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Record W7164621925

The effectiveness of different physical therapy methodologies in patients with lumbar radiculopathy caused by pain.

2021· dissertation· lt· W7164621925 on OpenAlexaboutno aff
Žagelytė, Iveta,

Bibliographic record

VenueInstitutional Repository of Utenos kolegija Higher Education Institution · 2021
Typedissertation
Languagelt
FieldBiochemistry, Genetics and Molecular Biology
TopicMyofascial pain diagnosis and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsLumbarTorsoLow back painLumbar spineBack painQuantitative sensory testingSensation
DOInot available

Abstract

fetched live from OpenAlex

The aim of the study – to evaluate the effectiveness of different physical therapy methodologies in patients with lumbar radiculopathy caused by pain. The tasks of the study are: 1. To evaluate the effectiveness of different physical therapy methodologies for pain in patients with lumbar radiculopathy caused by pain. 2. To evaluate the effectiveness of different physical therapy methodologies for back and abdominal muscle endurance in patients with lumbar radiculopathy caused by pain. 3. To evaluate the effectiveness of different physical therapy methodologies for lumbar spine mobility in patients with lumbar radiculopathy caused by pain. The participants of the study. Patients with lumbar radiculopathy caused by pain. The methodology of the study. SAS - digital pain scale to assess pain intensity, inclinometry - to measure lumbar spine movement amplitudes, Schober test - to assess lumbar spine mobility, Lovett test - to assess lower limb muscle strength, McGill torso test - tests to assess abdominal, dorsal muscle, back - to evaluate the influence of back pain on the patient's functional condition in various life situations, questionnaire survey. Conclusions of the study. Decreased sensation of pain, increased lumbar spine amplitude, mobility, muscle strength, abdominal and back static endurance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.010
GPT teacher head0.275
Teacher spread0.265 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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