Post-surgical rehabilitation for adults treated surgically for lumbar disc herniation: Systematic review and meta-analysis
Bibliographic record
Abstract
evaluated using the insertion torque?The purpose of this in vitro experiment was to correlate the screw insertion torque with a bending force necessary to create screw loosening in non-osteoporotic vertebrae.Methods: In total, 100 pedicle screws were implanted into both left and right pedicles of 50 single vertebrae (14 donors, mean age 41Æ8, T9-L4) with a median bone mineral density (BMD) of 145 (120-299) mgCaHA/cm3 assessed by QCT.After pre-drilling (2.5x20), the largest possible screw (5.5x40, 5.5x45, 6.5x40, 6.5x45, or 6.5x50) was implanted into the pedicle while measuring the insertion torque (Fig. 1A).Both endplates were embedded in PMMA and fixed in a material testing machine.A bending force was applied on the screw head until reaching a displacement of 1 mm (5 N pre-load; 10 mm/min; Fig. 1B).Afterwards, first a univariate and then a multiple mixed linear model were calculated (SPSS; significant level 0.05).Results: The bending force increased linearly with the insertion torque (multiple; p¼0.0002;Fig. 1C).The length and the diameter of the screw as well as the vertebral level had a significant impact on the bending force in the univariate model (p<0.0001)but not in the multiple one.BMD did not affect the bending force (univariate; p¼0.2483).The screw diameter showed a slight trend for the multiple model (p¼0.0584).Discussion: The correlation of insertion torque and bending force suggests an alternative prediction method for screw loosening in vivo.Additional information (e.g., need for augmentation) could be provided to the surgeon as well as surgical outcomes and patients' safety improved.This is potentially a simple, intra-operative, and radiation free method.Differences between the left and right pedicle, which could be due to the operator or the pedicle morphology, were statistically considered but were not relevant.Further experiments could investigate the influence of screw diameter or osteoporotic specimens.Here, the use of a dynamic testing method would provide a deeper mechanical understanding.Including osteoporotic specimens could, perhaps, indicate a minimum BMD level at which an impact in the clinic is likely to occur.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".