Bibliographic record
Abstract
Low back pain (LBP) is a major healthcare burden globally, but causation is still not fully understood. A fundamental aspect to be explored is the relationship between LBP and changes in movement and function. While adaptations have been observed, the direction and mechanisms of this interaction are unclear. The aim of this thesis was to improve our understanding of the methods used to investigate LBP and movement (Study 1 & 2), and to explore the relationship between LBP and movement/function (Study 3). Study 1 investigated the concurrent validity of skin-based sensors to measure spine kinematics and found that they should be considered to estimate intervertebral angles only. However, if their limitations are addressed appropriately, they can be a useful tool to monitor spine kinematics. Study 2 explored whether blood or brain markers are associated with heat/capsaicin pain. There were no differences between heat/capsaicin pain and placebo, suggesting an absence of the systemic neurophysiological response seen in clinical LBP. Study 3 explored the relationship between pain-states (pain vs no pain) of two types of LBP (recurrent and heat/capsaicin) and changes in movement/function. We found recurrent LBP to be associated with some changes that dissipate during recovery. However, these changes were not reproduced with experimental pain, indicating that it is not only the experience of pain that induced these changes. Additionally, when comparing outcomes between the recurrent LBP group and controls, differences between populations were found regardless of pain state. This suggests that the relationship between LBP and changes in movement/function is bi-directional and more complex than a causative one-directional relationship. Collectively, this thesis contributes to our understanding of current methods to understand LBP, including experimental pain and skin-based spine kinematics. We establish that pain-states in recurrent, but not experimental, LBP are associated with changes in movement and function. Additionally, there are differences between people with recurrent LBP and controls beyond the immediate experience of pain, highlighting the complexity of the interaction between LBP and movement/function. Advancing our methods to assess this relationship further will allow us to better tease out the reasons for the changes found.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".