Biomechanical assessments of lumbar spinal function. how low back pain sufferers differ from normals. implications for outcome measures research. part i: kinematic assessments of lumbar function
Bibliographic record
Abstract
OBJECTIVE: To review new and advanced biomechanical assessment techniques for the lumbar spine and illustrate the differences in lumbar function in patients with low back pain and asymtomatic subjects. DATA SOURCES: The biomedical literature was searched for research and reviews on spinal kinematic differences between low back pain subjects and healthy controls. A data search for articles indexed on MEDLINE until April 2002 was performed. RESULTS: Kinematic measurements of lumbar function were categorized into 3 areas where low back patients may differ from normals: (1) end range of motion during simple movements; (2) higher order kinematics (displacement, velocity, and acceleration) during complex movement tasks; and (3) spinal proprioception. The assessment of higher order kinematics during complex movement tasks is the most highly researched and the most successful in describing differences between the populations. The use of simple end range of motion appears questionable, while assessing spinal proprioception is the least researched, yet shows potential in highlighting differences between low back sufferers and asymptomatics. CONCLUSION: Current kinematic biomechanical assessment techniques are capable of identifying functional differences between low back pain populations and controls. The use and validity of the majority of these techniques as outcome measures are currently unknown, yet may be valuable in generating functional diagnoses, evaluating the mechanisms of current therapies, and prescribing specific rehabilitation programs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".