A review of activity monitors as a new technology for objectifying function in lumbar spinal stenosis
Bibliographic record
Abstract
The purpose of this review article is to introduce the concept of activity monitoring, and to discuss the application of accelerometry in rehabilitation research and clinical practice using lumbar spinal stenosis as a model. Function is a complex concept, and changes in function have historically been challenging to measure. The International Classification of Functioning (ICF) defines two distinct components of function: capacity and performance. Capacity, the ability to perform a given task in a controlled environment can be measured through any number of existing functional measures. Performance, defined as activities performed on a day to day basis in the context of real life is challenging to measure, yet important in identifying the impact of pathology on real life. Recent advances in technology have allowed us to begin to measure performance, using activity monitors (accelerometers). Activity monitoring has the potential to change our concepts of outcomes, and as a result, expand our ideas about appropriateness of interventions in rehabilitation. Researchers and clinicians might benefit from using the new technology of activity monitors to measure the impact of intervention and to assess function. Therefore, this review will discuss the concept of activity monitoring and highlight potential uses for activity monitors in spine research and clinical care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".