Surgical Treatment for the Painful Motion Segment
Bibliographic record
Abstract
STUDY DESIGN: A convenience literature-based review of the different techniques of posterior lumbar fusion. OBJECTIVE: To describe the history, specific techniques, and outcomes of different methods of posterior lumbar fusion. The specific methods that were described include 1) uninstrumented posterior, posterolateral, and facet fusion, and 2) instrumented fusion using pedicle screws or facet screws. SUMMARY OF BACKGROUND DATA: There are various posterior fusion techniques available for the treatment of degenerative lumbar spine conditions. Each individual technique has specific technical demands, indications, advantages, and disadvantages which should be taken into consideration when performing these procedures. METHODS: The published scientific literature on the different methods of posterior lumbar fusion was reviewed. The history, indications, advantages, disadvantages, and clinical and radiographic outcomes were described based on the literature search. RESULTS/CONCLUSIONS: Posterior fusion techniques have been and will continue to be among the most commonly performed procedures in lumbar spine surgery. The different methods of fusion are well defined, as are the possible complications and outcomes. They are effective techniques when performed on appropriately selected patients by a surgeon knowledgeable in the techniques and indications. Further studies are needed regarding promising but relatively unproven developments such as minimally invasive surgery and the use of osteoinductive agents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.013 | 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".