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Record W2766094889 · doi:10.3138/ptc-2019-0089

Ability of Physiotherapists and Physiotherapy Students to Evaluate and Classify Lumbar Movement Control Using Lumbopelvic Movement Control Tests

2020· article· en· W2766094889 on OpenAlexvenueno aff
Cornelia Huysamen, Benita Olivier, Vaneshveri Naidoo, Aimée Stewart

Bibliographic record

VenuePhysiotherapy Canada · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyLow back painSittingStraight leg raiseLumbarManual therapyPhysical medicine and rehabilitationFunctional movementTest (biology)Observational studyMovement controlRange of motionSurgery

Abstract

fetched live from OpenAlex

Purpose: The aims of our study were to (1) describe the tests that physiotherapists use to assess lumbopelvic movement control and (2) compare physiotherapists’ and physiotherapy students’ ability to evaluate lumbar movement dysfunction in patients with non-specific low back pain (NSLBP). Method: A quantitative, observational, cross-sectional study design was used. A total of 93 qualified physiotherapists and 96 students participated in our study. The physiotherapists reported whether they were familiar with lumbopelvic movement control tests and indicated which tests they used to assess lumbopelvic movement control (Part 1). Both the physiotherapists and the students evaluated and classified lumbopelvic movement control while observing videos of patients with NSLBP (Part 2). The responses from physiotherapists and students were described and compared between the two groups using the Fisher exact test ( p ≤ 0.05). Odds ratios were calculated in terms of years of experience and level of education. Results: A total of 80 physiotherapists (86%) were familiar with lumbopelvic movement control tests, but only a few were using those tests as part of the assessment of patients; 12 (13%) used waiter’s bow; 16 (17%) used rocking forward; 17 (18%) used rocking backward; 32 (34%) used sitting knee extension; 34 (37%) used prone knee flexion; and 70 (75%) used posterior pelvic tilt. The physiotherapy students and qualified physiotherapists generally agreed on the ratings of most of the videos (e.g., as correct or incorrect). A difference was found in only 3 of 24 videos for both the qualified physiotherapists and the physiotherapy students ( p = 0.001, p = 0.007, and p = 0.033, respectively), which indicates that qualified physiotherapists and students agreed and classified 21 videos the same. No differences were found in the ability of physiotherapists (regardless of experience) or students to classify patients as presenting with either a flexion or an extension pattern. Conclusions: The majority of physiotherapists were familiar with lumbopelvic movement control tests, but only a few used them. Both the physiotherapists and the students were able to classify patients with NSLBP presenting with either a flexion or an extension pattern.

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.323
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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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