Are non-steroidal anti-inflammatory drug injections an alternative to steroid injections for musculoskeletal pain?: A systematic review
Bibliographic record
Abstract
BACKGROUND: Given the potential side effect profile of steroids, the need for an alternative injectable anti-inflammatory is needed. The purpose of this systematic review was to compare corticosteroid injections with non-steroidal anti-inflammatory drug (NSAID) injections for musculoskeletal pain. METHODS: Reviewers with methodological and content expertise searched three databases: PUBMED, Medline and EMBASE. Two blinded reviewers searched, screened, and evaluated the data quality. Data was abstracted in duplicate. Agreement and descriptive statistics are presented. RESULTS: Four studies were included. All four studies found no statistically significant differences in improvements on the visual analog scale. The follow-up period within the four studies ranged between 2 weeks and 3 months. No statistically significant differences were demonstrated between the two groups with regards to functional outcomes. INTERPRETATION: The studies reviewed, while limited in quantity, show that compared with corticosteroids, NSAIDs provide equivalent, if not better, pain relief from the musculoskeletal ailments assessed. Further, there is weak evidence supporting a lower recurrence rate of symptoms with NSAIDs when compared to corticosteroids. There is a need for more long-term high-quality studies on this topic. LEVEL OF EVIDENCE: Level II (Systematic review of Level II and III studies).
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".