P.095 Soft tissue preserving direct multilevel pars repair using the ‘Smiley Face’ technique with 3D optical imaging based intraoperative spinal navigation
Bibliographic record
Abstract
Background: Two broad categories for the surgical management of symptomatic spondylolysis exist: decompression, or direct reduction and fixation. Direct fixation can maintain mobility and leads to improved outcomes over spinal fusion. The ‘smiley face’ technique is a direct fixation method of pars defect repair that uses one bent rod to reduce the number of linkage points and simplify the construct. Methods: Bilateral pars defects at L3 and L5 were repaired using the ‘smiley face’ technique. Patient reported outcomes, including the Oswestry Disability Index (ODI) and visual analog scale (VAS) scores for back and leg pain were assessed preoperatively and again at 6 weeks postoperatively. Results: The patient underwent a soft tissue preserving multi-level bilateral L3 and L5 pars defect repair using the smiley face technique while utilizing radiation-free 3D optical imaging to capture multiple points for registration despite minimal laminar exposure. The patient’s ODI and lower back VAS scores decreased from 25 to 8 and 7.5 to 4 respectively, after surgery, correlating to an excellent outcome on ODI. Conclusions: The smiley face technique can be used with soft tissue preserving techniques to achieve adequate bony reduction while maintaining intersegmental mobility in patients with multi-level pars defects. 3D imaging allows soft tissue preservation with increased registration points for intraoperative navigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".