Charcot Arthropathy of the Lumbar Spine Treated Using One-Staged Posterior Three-Column Shortening and Fusion
Bibliographic record
Abstract
STUDY DESIGN: Case report. OBJECTIVE: We present a case of lumbar Charcot arthropathy successfully treated surgically using posterior 3-column resection, spinal shortening, and fusion. SUMMARY OF BACKGROUND DATA: The operative treatment of Charcot arthropathy of the spine has conventionally been a combination of anterior and posterior surgery. The morbidity associated with these surgical procedures can be considerable. A posterior-only approach to the problem would avoid the additional morbidity associated with an anterior approach. We present a case of lumbar Charcot arthropathy with deformity treated successfully using such a procedure. METHODS: Discussion of the patient's clinical and radiologic history, the technical merits of the operative intervention and a review of the relevant background literature are presented. RESULTS: A multilevel, single-stage, posterior 3-column resection with primary shortening and instrumented fusion augmented with rhBMP2 in a multiply operated patient with deformity provided a optimal biologic and mechanical environment for healing of the Charcot arthropathy and improved the sagittal and coronal profile of the spine. CONCLUSION: A single-stage, multilevel, posterior 3-column resection and primary shortening can be a useful surgical strategy in symptomatic patients with Charcot arthropathy of the spine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".