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Record W7000910243

Hip & Spine Mechanics - Understanding the linkage from several perspectives of injury mechanisms to rehabilitation using biomechanical modelling

2019· dissertation· en· W7000910243 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2019
Typedissertation
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationKinematicsBiomechanicsPopulationValgusAnterior cruciate ligament
DOInot available

Abstract

fetched live from OpenAlex

One recent megatrend in medicine is that of “precision medicine” whereby a precise diagnosis leads to a precise intervention for superior results. This thesis was undertaken to enhance the understanding of spine and hip interactions and facilitate precision in both detection and the intervention of mechanical and neurological based disorders. The hip and spine are highly integrated structures. In order to adequately examine and improve the understanding of the complex mechanical linkage between the two, development of a highly biofidelic Hip-Spine Model (HSM) was pursued. Given that no model existed that incorporated the necessary detail, several challenges regarding model development needed to be addressed. It was clear that biofidelity of the model depended on a better understanding and representation of the passive hip stiffness in both males and females. Thus, the experimental data of passive stiffness was evaluated in conjunction with the HSM passive stiffness model predictions. Next, known Anterior Cruciate Ligament (ACL) injury risk factors, such as dynamic knee valgus (DKV) were examined in a female population where both the kinetic and kinematic variables of the hip and spine were evaluated to assist in differentiating those deemed at-risk and not-at-risk during the drop vertical jump (DVJ) procedure. Finally, an atlas of rehabilitation exercises was constructed to guide program design and progression/regressions of rehabilitation protocols for those with back and hip concerns. Each of these themes were unified around the overall goal of this thesis, that being the understanding of the hip-spine mechanical linkage pertaining to injury mechanisms and a guide for rehabilitation of hip-spine disorders. \nThe first task was the development of the HSM. This model is anatomically detailed and driven by biological signals obtained from the individual to provide new insights into the understanding of the linkage in a way that was sensitive to the unique movement strategies of the individual. The HSM is an expansion of the previously established ‘Spine Model’ (SM), developed by Stuart McGill and his team over the past 37 years. The model anatomy was expanded from the current SM using the most complete single subject lower limb data set available know as the Twente Lower Extremity Model (TLEM). Hip ligaments were also added to enhance the passive behaviour of the model. An electromyographic (EMG) driven approach with subject specific kinematics was used to compute the model outputs that consisted of tissue and joint loads. Thus, the first objective of this thesis was to examine the interactions of hip and spine mechanics using a newly developed Hip-Spine Model (HSM) and then to investigate a spectrum of injury mechanisms and rehabilitation exercises. \nThe next objective was to evaluate passive hip stiffness to enhance the biofidelity of the model and the understanding of hip mechanics in both males and females. A novel testing apparatus was designed and fabricated for measuring hip stiffness which could easily be adapted to clinical settings. This study also serves to establish normative baselines for passive hip stiffness in vivo. \nThe third objective of this thesis was to examine issues of normal function and potential injury mechanisms. For example, ACL injury risk has been linked with some knee kinematic and kinetic patterns however, hip and spine interactions have not been appropriately explored nor have neuromuscular control strategies. This thesis linked the mechanical variables which differentiate at-risk landings of the DVJ task versus non-at-risk landing in females to enhance the understanding of risk behaviours. Clear differences in hip and spine control were documented to differentiate high and low valgus landings. Adding this knowledge to the current understanding of ACL injury risk will lead to the development of superior and more specific coaching cues to decrease tissue stress/strain concentrations. This approach will underpin intervention strategies leading to lower risk behaviour and correspondingly lower injury risk among female athletes. \nThe final objective of this thesis was to evaluate the appropriateness of rehabilitation exercises to address hip spine disorders. Currently, there exists a myriad of exercises but little evidence to guide clinical reasoning or decisions for exercise choice, progression, volume and technique. Currently missing from the literature is knowledge of tissue and joint loads in combination with muscle activation patterns. This knowledge will facilitate better matching of specific exercises for specific disorders. \nThe main objectives of this thesis were: (1) The development of the anatomically detailed, biologically driven HSM which successfully computes joint and tissue loads unique to the individual and their neuromuscular control strategy. (2) The establishment of passive hip stiffness for males and females. (3) To enhance understanding of both neurological and tissue loading characteristics associated with ACL injury risk which when added to the current knowledge provides the opportunity for more precise interventions. (4) The beginning of the development of an atlas for rehabilitation exercises to guide prescriptions that can be matched to specific hip-spine disorders. These findings have the potential to enhance precision medicine in the area of musculoskeletal health – where precision medicine is currently lacking.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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