Fluoroscopic Computer-Assisted Pedicle Screw Placement Through a Mature Fusion Mass
Bibliographic record
Abstract
In Brief Study Design. Observational matched cohort study with computed tomography (CT) analysis of pedicle screw placement. Objective. Assess the clinical accuracy of computer-assisted fluoroscopy for the placement of thoracolumbar pedicle screws through a mature posterolateral fusion mass. Summary of Background Data. Pedicle screw placement through an amorphous posterolateral fusion mass intuitively carries a higher risk of pedicle wall violation. Methods. Postoperative CT scans of 102 pedicle screws placed through a mature posterolateral fusion mass (n = 10 [T10–T12]; n = 92 [L1–S1]) were independently assessed and compared with a matched control (nonobscured anatomy) group. All screws were placed before any decompression using the FluoroNav system. Results. In the fusion mass group, overall 81.4% of screws were completely within the pedicle. Seven medial and 12 lateral pedicle breaches occurred. Relative to the total number of screws, pedicle breaches were graded II (<2 mm) in 13.5%, III (2–4 mm) in 2.9%, and IV (>4 mm) in 2.0% of screws. The number and direction of pedicle breaches were not significantly different when compared with the control group. There were no clinically significant screw misplacements in either group. Conclusions. The use of computer-assisted fluoroscopy is safe and effective for the placement of thoracolumbar (T10–S1) pedicle screws through a posterolateral fusion mass without performing laminoforaminotomies. In this series of 102 pedicle screws (T10–S1) placed through a fusion mass using computer-assisted fluoroscopy (FluoroNav) without laminoforaminotomies, the accuracy was equivalent to a matched cohort with nonobscured anatomy (81.4% vs. 84.3% of screws completely within the pedicle). The majority (74%) of pedicle breaches were minor (<2 mm) and none was associated with clinical sequelae.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".