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Record W2308914881 · doi:10.3138/ptc.2014-62-cc

Clinician's Commentary on Keep et al.

2016· letter· en· W2308914881 on OpenAlexafffundvenue
Nadia Keshwani

Bibliographic record

VenuePhysiotherapy Canada · 2016
Typeletter
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsQueen's University
FundersQueen's University
KeywordsComputer scienceMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Quantification of hip extensor strength is often of interest to physiotherapists, as strength deficits are associated with conditions such as low back pain and hip osteoarthritis and may lead to impairments in physical functioning.Physiotherapists typically use manual muscle testing to assess muscle strength, but this grading scale is somewhat subjective and may fail to detect small changes in strength that are nevertheless clinically significant.There is an ongoing push in the physiotherapy community to adopt more objective, sensitive, and specific outcome measures, both to instil greater confidence in our assessment findings and to better evaluate treatment effectiveness.Handheld dynamometry (HHD) provides a more objective measure of strength that is portable, relatively inexpensive, and easy to use.Because the resulting value is on a continuous measurement scale (i.e., kilograms or newtons), HHD may allow us to better to detect strength changes over time in response to muscle training or disuse.Before HHD is adopted into clinical practice to quantify muscle strength, we must first have evidence that its results are both valid and reliable.While investigations into the use of HHD to quantify hip extensor strength have typically positioned patients in prone, Lu and colleagues 2 have suggested that the ''prone standing'' position may be more appropriate.In this position, the patient stands with the upper body supported by a plinth and the standing leg, while the leg being tested is positioned in 45 of hip flexion and knee extension.Compared to prone lying, this position optimizes the length-tension curve of the hip extensor musculature and more closely mimics the functional tasks likely to be affected.Keep and colleagues' study, which investigated the validity of hip extensor strength measurements obtained in the prone standing position using HHD, found moderate correlations (r ¼ 0.51) between values obtained with HHD and those obtained with an isokinetic dynamometer. 1 Studies such as Keep and colleagues', although often overlooked by physiotherapists, are integral to the advancement of our field.So what can we take from the current study?When HHD is widely integrated into clinical practice in the near future, should we adopt the prone standing position over the prone position when testing hip extensor strength?Certainly, Keep and colleagues have generated sufficient evidence to demonstrate that HHD in the prone standing position produces a valid measure of hip extensor strength. 1Furthermore, testing in this position seems to be more functionally relevant, as the patient is in a modified upright position with the hip being tested in flexion.Because most functional deficits will manifest in standing activities (e.g., stair climbing, walking) and will involve hip extensor activation at some degree of flexion, prone standing does seem to be a more logical choice than prone lying.However, other factors must also be considered.People who are likely to have hip extensor strength deficits (e.g., those with low back pain) may not have sufficient range of motion to allow the degree of lumbar flexion required for prone standing, and for those who do have the necessary range, the prone standing position may provoke pain.Further, for people with balance deficits or dizziness, the prone standing position may compromise safety.In such cases, it may be preferable to test hip extensor strength in prone lying.

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)0.004
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.064
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0050.004
Scholarly communication0.0040.005
Open science0.0050.002
Research integrity0.0640.041
Insufficient payload (model declined to judge)0.0160.017

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 head0.016
GPT teacher head0.315
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2016
Admission routes3
Has abstractyes

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