Structural health monitoring (vibration) as a tool for identifying structural alterations of the lumbar spine: a twin control study
Bibliographic record
Abstract
Structural health monitoring (SHM) is an engineering technique used to identify mechanical abnormalities not readily apparent through other means. Recently, SHM has been adapted for use in biological systems, but its invasive nature limits its clinical application. As such, the purpose of this project was to determine if a non-invasive form of SHM could identify structural alterations in the spines of living human subjects. Lumbar spines of 10 twin pairs were visualized by magnetic resonance imaging then assessed by a blinded radiologist to determine whether twin pairs were structurally concordant or discordant. Vibration was then applied to each subject's spine and the resulting response recorded from sensors overlying lumbar spinous processes. The peak frequency, area under the curve and the root mean square were computed from the frequency response function of each sensor. Statistical analysis demonstrated that in twins whose structural appearance was discordant, peak frequency was significantly different between twin pairs while in concordant twins, no outcomes were significantly different. From these results, we conclude that structural changes within the spine can alter its vibration response. As such, further investigation of SHM to identify spinal abnormalities in larger human populations is warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".