Sonographically Guided Posterior Subtalar Joint Injections via the Sinus Tarsi Approach
Bibliographic record
Abstract
OBJECTIVES: To determine the feasibility and accuracy of sonographically guided posterior subtalar joint (PSTJ) injections performed through the sinus tarsi. METHODS: A single experienced operator completed 10 sonographically guided PSTJ injections via the sinus tarsi on 10 unembalmed cadaveric ankle-foot specimens. Injections were performed using a 17-5-MHz linear transducer, a 25-gauge, 50-mm needle, and an out-of-plane, anterior-to-posterior needle trajectory parallel to the calcaneal surface. Sonographic assessment for fluid in the posterior and lateral PSTJ recesses, sinus tarsi, and peroneal tendon sheath was performed before and after injections of 2 and 4 mL of tap water. Two additional specimens were injected with a contrast agent: 1 via the sonographically guided approach and another by a computed tomographically guided approach. RESULTS: All 10 sonographically guided PSTJ tap water injections were accurate, distending both the posterior and lateral PSTJ recesses. In addition, all 10 specimens showed posterior recess distension by 2 mL, whereas only 2 specimens (20%) showed lateral recess distension at this volume. By 4 mL, both recesses were clearly distended in all specimens. Both contrast agent injections produced similar PSTJ computed tomographic arthrograms and patterns of recess distension similar to the sonographically guided tap water injections. No sonographically guided PSTJ injection placed fluid in the peroneal tendon sheath. CONCLUSIONS: Sonographically guided PSTJ injections via the sinus tarsi can accurately and specifically deliver injectate into the PSTJ while monitoring injectate flow within the posterior recess. The sinus tarsi approach may be used as an alternative technique to perform sonographically guided PSTJ injections when clinically appropriate.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".