Proximal facet joint violation and breaches after percutaneous insertion of 311 lumbar pedicle screws using the pedicle axis fluoroscopic view
Bibliographic record
Abstract
Introduction: Violation of the non-fused proximal facet joints (PFJ) above instrumentation might be associated with accelerated arthritis and adjacent-segment disease. Standard fluoroscopic views do not allow for an exclusion of PFJ violation and have been associated with high rates of this complication. Research question: We adopted the use of the pedicle axis view (PAV) and investigated our results and potential correlations. Materials and methods: We performed a retrospective cohort study of cases of percutaneous pedicle screw insertion in the lumbar spine, using the PAV. Various factors were investigated on postoperative CT scans, e.g. presence of PFJ violation, PFJ angles and analysis of breaches. Results: Overall, 311 screws were inserted using the PAV. The percentage of screws that resulted in PFJ violation was 3.7 % (n = 6). Higher PFJ angles played a role with an odds ratio of 1.21 (95 % CI: 1.03-1.43). The majority of the screws (68.1 %) did not cause cortical breaches. Regarding the rates of breaches, 14.9 % were minor cortical breaches and 11.6 % were moderate. 1.9 % of the screws caused severe breaches, but none of those were located medially or inferiorly. None of the observed breaches led to new symptoms or revision. Discussion and conclusion: The adoption of the fluoroscopic PAV for percutaneous lumbar pedicle screws led to low rates of proximal facet joint violation and severe breaches. Moreover, PFJ violation was more prevalent with higher PFJ angles and surgeons should remain vigilant in such cases. None of the observed breaches were clinically relevant.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".