Transmission of Force in the Lumbosacral Spine During Backward Falls
Bibliographic record
Abstract
STUDY DESIGN: Mathematical model, combined with and verified using human subject data. OBJECTIVE: (1) To develop and verify a lumped-parameter mathematical model for prediction of spine forces during backward falls; (2) to use this model to evaluate the effect of floor stiffness on spine forces during falls; and (3) to compare predicted impact forces with forces previously measured to fracture the spine. SUMMARY OF BACKGROUND DATA: Vertebral fractures are the most common osteoporotic fractures and commonly result from falls from standing height. Compliant flooring reduces the force at the ground during a backward fall from standing; however, the effect on spine forces is unknown. METHODS: A 6-df model of the body was developed and verified using data from 10 human subjects falling from standing onto 3 types of compliant floors (soft: 59 kN/m, medium: 67 kN/m, and firm: 95 kN/m). The simulated ground forces were compared with those measured experimentally. The model was also used to assess the effect of floor stiffness on spine forces at various intervertebral levels. RESULTS: There was less than 14% difference between model predictions and experimentally measured peak ground reaction forces, when averaged over all floor conditions. When compared with the rigid floor, average peak spine force attenuations of 46%, 43%, and 41% were achieved with the soft, medium, and firm floors, respectively (3.7, 3.9, 4.1 kN vs. 6.9 kN at L4/L5). Spine forces were lower than those at the ground and decreased cranially (4.9, 3.9, 3.7, 3.5 kN at the ground, L5/S1, L4/L5, and L3/L4, respectively, for the soft floor). CONCLUSION: Lowering the floor stiffness (from 400 to 59 kN/m) can attenuate peak lumbosacral spine forces in a backward fall onto the buttocks from standing by 46% (average peak from 6.9 to 3.7 kN at L4/L5) to values closer to the average tolerance of the spine to fracture (3.4 kN).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".