Routine Imaging for Elective Lumbar Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional, questionnaire study. OBJECTIVE: To characterize imaging practices for 3 common lumbar spine procedures. SUMMARY OF BACKGROUND DATA: As lumbar surgical procedures are performed with increasing frequency, it becomes incrementally more important to optimize patient care, minimize risk, and reduce associated costs. Imaging is an area for potential improvement; however, little has been done to characterize current imaging practices, compare imaging practices with current evidence, or establish a standard of care. METHODS: We distributed a single-page questionnaire to all attending spine surgeons at a United States spine conference (The Spine Study Group) in 2012. RESULTS: Forty-one of 74 surgeons (55.4%) completed and returned the questionnaire. All results are given for posterior lumbar decompression, posterior lumbar fusion, and anterior lumbar fusion, respectively.Intraoperatively, 75%, 90%, and 95% of surgeons use fluoroscopy, whereas 25%, 10%, and 5% use plain film; 80%, 59%, and 54% take images prior to skin incision; 59%, 98%, and 100% always take final images at the end of the procedure while still in the operating room. Postoperatively, 13%, 54%, and 54% of surgeons take images after patients have left the operating room but before they have been discharged. Interestingly, 10%, 50%, and 51% of surgeons not only take intraoperative images of their final constructs, but also take additional images before discharge.Surgeons follow their postoperative outpatients with imaging for a mean of 0.4, 1.5, and 1.5 years. Fifty-four percent, 98%, and 100% follow with anterior-posterior views; 56%, 93%, and 95% with lateral views; and 15%, 39%, and 39% with flexion-extension films. For both anterior and posterior fusion, 26% routinely follow with computed tomographic scan to assess fusion. CONCLUSION: Findings highlight extreme variability in practice associated with a notable lack of standard of care and provide a baseline for utility studies that may lead to more evidence-driven care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".