British Journal of Rheumatology 1996;35:1269-1273 THE VALUE OF SPECT SCANS IN IDENTIFYING BACK PAIN LIKELY TO BENEFIT FROM FACET JOINT INJECTION
Bibliographic record
Abstract
Lumbar facet disease is sometimes implicated in low back pain. Identification is difficult and this may account for a variable response. Single photon emission computerized tomography (SPECT) is a scanning technique which enables localization of facet joint pathology. We determined whether recognition of facet disease by this method improved the response to corticosteroid injection treatment. Fifty-eight patients with low back pain and displaying accepted clinical criteria for facet joint disease were evaluated by SPECT. Twenty-two had facetal uptake of isotope. These and the tender facet joints of 36 scan-negative patients were injected with 40 mg methylprednisolone and 1 ml 1 % lignocaine under X-ray control. Pain was assessed by a blind observer using the McGill questionnaire (MGQ), Present Pain Intensity score (PPI) and a Visual Analogue Scale (VAS). VAS, PPI and MGQ were reduced in the scan-positive patients at 1 month (P = 0.05, P = 0.0005, P = 0.005) and MGQ at 3 months (P = 0.01), whilst scan-negative patients were unchanged. The percentage of scan-positive patients who reported improvement was 95% at 1 month and 79 % at 3 months, significantly greater than the control group (P = 0.0005, P = » 0.01). Within 6 months, pain improvement in the SPECT-positive group was no longer statistically significant. Tenderness did not correlate with increased uptake on SPECT scan. Osteoarthritis of the facets was more common in the SPECT-positive patients (P < 0.001), but did not correspond with sites of increased uptake on SPECT scan. These results suggest that SPECT can enhance the identification of back pain sufferers likely to obtain short-term benefit from facet joint injection. KEY WORDS: Imaging, Bone scan, Spinal pain, Treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".