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Record W2735892943 · doi:10.51415/10321/2101

The effectiveness of combined spinal manipulation and patella mobilization compared to patella mobilization alone in the conservative management of patellofemoral pain syndrome

2000· dissertation· en· W2735892943 on OpenAlexaboutno aff
Neil Osmond Stakes

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatellaPatellofemoral pain syndromePhysical therapyMobilizationAnterior knee painInformed consentSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

Purpose. Patellofemoral pain syndrome (PFPS) refers to a syndrome associated with the following signs and symptoms: anterior knee pain, inflammation, imbalance, instability, or any combination thereof (Wood 1998). The purpose of this investigation was to evaluate whether spinal manipulation, as an adjunct to patella mobilization, contributed significantly to the improvement of patients diagnosed with PFPS. A prospective trial using convenient sampling was implemented using the first 60 volunteers that met the requirements. These were randomly divided into two groups. Participants in group 2 received combined patella mobilization and spinal manipulative therapy, while those in group 1 received patella mobilization only. Each patient selected for the study was required to complete an informed consent form. The selected patients underwent a general medical case history, lower back and knee orthopaedic regional examinations. 8 clinical experiments were done: pain threshold (ALGI), pain tolerance (ALG2), the mean least pain experienced (NRS 1), the mean worst pain experienced (NRS2), the mean pain experienced (NRS3), pain quality (McGill), patellofemoral joint evaluation scale (PFJE) and a patient specific functional scale (PSFS). All were continuous variables except McGill, which was a categorical variable. For each clinical experiment, readings were taken 3 times, i.e. at the first, third and sixth consultations

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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

Citations5
Published2000
Admission routes1
Has abstractyes

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