Quantifying and predicting upper limb capability and dysfunction of breast cancer survivors
Bibliographic record
Abstract
Breast cancer will affect one in every nine Canadian women during their lifetime. As diagnostic and treatment methods improve, survival rates are approaching 90%. However, an alarming 30-82% of survivors suffer from persistent upper limb morbidity as a result of their cancer treatments (Kwan, Jackson, Weir, Dingee, McGregor, & Olivotto, 2002; Lauridsen, Overgaard, Overgaard, Hessov, & Cristiansen, 2008; Rietman, Dijkstra, Debreczeni, Geertzen, Robinson, & De Vries, 2004). These persistent disabilities compromise function and quality of life, hindering survivors from returning to work and leading functional and independent lives. In order to prevent and rehabilitate upper limb morbidities, quantification of the physical capabilities of this population must occur. \nThis thesis quantified the upper limb physical capabilities and limitations of breast cancer survivors by producing the most comprehensive collection of 3-D kinematics, muscle activation patterns, muscle-specific strength, and quality of life and disability measures during a wide range of functional tasks. Compared to the contralateral limb, the affected side demonstrated reduced humeral elevation (-6.5°) and external rotation angles (-8.9°), increased humeral internal rotation (+13.1°), reduced scapular protraction (-3.9°) (although both sides demonstrated protraction), increased upward rotation (+2.8°) and increased posterior tilting (+4.1°) of the scapula. These relationships varied with the task being performed. Muscle activation patterns revealed increased total muscle effort on the affected side during work tasks (p = 0.0258), and reductions in pectoralis major sternal activation (p<0.0001 – p = 0.0230). Increased muscle effort, weakness and discomfort levels were evident in both primary and secondary muscles (muscles outside the field of surgery and radiation). \nHumeral internal (IR) and external rotation (ER) co-activation was defined in both healthy (H) and breast cancer survivor populations (BCP) (H: r2 = 0.70 (IR) and 0.35 (ER); BCP: r2 = 0.77 (IR) and 0.77 (ER)). Humeral abduction angle and task intensity were important factors in the prediction of co-activation in both populations. Inclusion of physiological cross-sectional area (PCSA) weightings did not sufficiently improve the representation of co-activation in the healthy population. Healthy co-activation relationships were successfully extrapolated to a novel set of IR exertions (r2 = 0.76 IR exertions; r2 = 0.40 ER exertions). Comparisons made between populations identified differing muscle strategies used by survivors to maintain glenohumeral joint stability. Compared to healthy population co-activation, the survivors demonstrated greater activation of IR and ER muscles during their respective rotation type. Survivors demonstrated increased (≤8.7%) activation of pectoralis major muscle activation compared to the healthy population. \nAn optimization-based muscle force prediction model was used to reflect specific muscle dysfunction of the pectoralis major muscles, and population-specific co-activation was enforced as a constraint. Empirically measured EMG was more closely associated to muscle force predictions of external rotator muscles (r = 0.567) than internal rotator muscles (r = 0.347). Model predictions were influenced by exertion type, co-activation constraint, hand force and pectoralis major capability constraints. The model predicted muscle forces more closely to empirical measurements of activation when the co-activation constraint was enforced, emphasizing the importance of consideration of antagonistic muscle activation in biomechanical modeling. \nThis comprehensive description of physical capabilities of the breast cancer population has never been performed in such detail. This body of work has furthered the knowledge available regarding the capacity and functional limitations of survivors and preliminary recommendations regarding therapeutic treatment and directions for future works have been suggested. The continued development of this research and future application of interventions designed to address these disabilities will promote the eventual return to function and work of survivors through targeted rehabilitation and treatment strategies. Development of effective rehabilitation and prevention strategies could potentially lower the social and economic burdens of survivor aftercare and dramatically enhance the quality of life of survivors, allowing them to lead highly functional and independent lives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".