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Record W7164629024

Physical therapy effect on pain and waist functional condition for patients with lumbar hernia.

2021· dissertation· lt· W7164629024 on OpenAlexaboutno aff
Fedaravičiutė, Agnė,

Bibliographic record

VenueInstitutional Repository of Utenos kolegija Higher Education Institution · 2021
Typedissertation
Languagelt
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsLumbarWaistTorsoLow back painBack painQuality of life (healthcare)
DOInot available

Abstract

fetched live from OpenAlex

Relevance of the topic. Lower back pain (LBP) is a sensitive problem [1]. Studies show that between 60% and 80% of people have felt back pain [3]. Research data have shown that back pain affects both sexes, both men and women, but men suffers pain much more often than women due to harder physical work [16]. Spinal diseases progress to incapacitation and one of the most common pathologies is spinal hernia. The main goals of physical therapy are to restore lost movements, reduce pain intensity, improve functional abilities and quality of life [21]. Research aim - To evaluate the effect of physical therapy on pain and waist functional condition in patients with lumbar hernia. Research tasks: 1. To evaluate the effect of physical therapy on pain intensity in patients with lumbar hernia. 2. To evaluate the effect of physical therapy on lumbar spine mobility in patients with lumbar hernia. 3. To evaluate the effect of physical therapy on the static endurance of the waist muscles in patients with lumbar hernia. The study included 14 patients with lumbar hernia. Used assessment methods: Digital Analog Pain Scale (DAPS) - to assess pain intensity, modified Schuber sample - to determine lumbar spine mobility, McGill test - to assess static torso muscle endurance. Research results: After physical therapy procedures, the intensity of pain decreased, the mobility of the lumbar spine improved, and the static endurance of the waist muscles improved.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.274
Teacher spread0.267 · 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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