Ultrasound‐Guided Cervical Facet Joint Injections
Bibliographic record
Abstract
OBJECTIVES: To evaluate the feasibility of ultrasound (US)-guided cervical facet injections and to identify the potential obstacles to routine use of this technique. METHODS: After Institutional Review Board approval, 4 cadavers were used in this study. Age, sex, body mass index, and neck circumference were recorded. A total of 40 facet injections were performed from C2-C3 to C6-C7 under US guidance with radiodense colored latex. Visibility of cervical tissues and the needle was graded as complete, partial, or null (no injection was performed in this case). Frontal and lateral radiographs were taken, followed by cadaveric dissection to assess contrast and the latex distribution, which were recorded as intra-articular (success), peri-articular (success), or absent (failure). A 2-tailed Fisher exact test and Pearson χ(2)test were used to evaluate difference between success and failure rates for qualitative variables. RESULTS: Seventy-eight percent (31 of 40) of US-guided facet joint injections were successful. No statistically significant differences were found regarding body mass index, neck circumference, needle caliber, operators, and between left and right sides. All failures involved C2-C3 and C6-C7 levels, and this result was statistically significant (Pearson χ(2) = 20.645; P < .001). CONCLUSIONS: Although US-guided cervical facet joint injections are feasible, substantial obstacles may prevent their routine use. The main obstacle is to effectively identify and target the correct cervical level in a prone position.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".