Does the Removal of Spinal Implants Reduce Back Pain?
Bibliographic record
Abstract
BACKGROUND: The importance of the removal of spinal implants is known in the presence of infection. However, the benefits and/or risks of the removal of spinal implant for the management of back pain are not clear. METHODS: In this retrospective study, we aimed to evaluate the beneficial effects of the removal of spinal implants for back pain. Study included 25 patients with thoracolumbar instrumentation. RESULTS: Seventeen (68%) of them were male. Indications for spinal instrumentation were vertebra fracture (n = 9), iatrogenic instability due to multiple segment laminectomy (n = 12), and instrumentation after recurrent disk herniations (n = 4). Mean visual analog score (VAS) before the removal was 8.08. Mean VAS was 3.36 after the removal. Spinal instruments were removed after the observance of the presence of fusion. All patients were prescribed analgesics and muscle relaxants for 3 weeks before removal. Back pain did not decrease in five (20%) patients in total. Four of them had been instrumented due to recurrent lumbar disk herniation. None of the patients reported the complete relief of pain. CONCLUSION: In conclusion, patients should be cautioned that their back pain might not decrease after a successful removal of their instruments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".