Use of Minimally Invasive Surgical Techniques in the Management of Thoracolumbar Trauma
Bibliographic record
Abstract
In Brief Study Design. Literature review and expert opinion. Objective. To provide an overview of the current concepts of minimally invasive surgical (MIS) techniques for the management of thoracolumbar (TL) spinal trauma. Summary of Background Data. Current surgical treatment of thoracolumbar trauma typically involves open placement of spinal instrumentation with fusion. Conventional open spinal exposures can be associated with significant muscle morbidity that can lead to subsequent paraspinal muscular atrophy, scarring, decreased extensor strength and endurance, as well as pain. This approach-related morbidity is the main impetus for application MIS techniques to spinal procedures including trauma. Methods. A review of the relevant English literature was performed. Results. The current rationale, clinical applications, outcomes, and limitation of MIS management of TL injuries are summarized. Conclusion. The application of MIS techniques to spinal trauma is theoretically sound. However, the indications and technology are currently in evolution. Although very limited information is available, the results of current MIS techniques for the management of TL trauma are encouraging. This paper provides an overview of the rationale, applications and limitations of the use of minimal invasive surgical (MIS) techniques and tools for the management of select spine trauma patients. The goal of applying these MIS techniques to trauma is to reduce approach-related surgical morbidity while maintaining the traditional surgical principles of spinal trauma management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".