A novel infrapatellar approach of ultrasound-guided intra-articular injection of the knee from both lateral and medial side: a case series
Bibliographic record
Abstract
Knee osteoarthritis (OA) is common. Ultrasound-guided intra-articular injection (UGIAI) using the superolateral approach is currently the gold standard for treating knee OA, but it is not 100% accurate, especially in patients with no knee effusion. Herein, we present a case series of chronic knee OA treated with a novel infrapatellar approach to UGIAI. Five patients with chronic grade 2-3 knee OA, who had failed on conservative treatments and had no effusion but presented with osteochondral lesions over the femoral condyle, were treated with UGIAI with different injectates using the novel infrapatellar approach. The first patient was initially treated using the traditional superolateral approach, but the injectate was not delivered intra-articularly and became trapped in the pre-femoral fat pad. The trapped injectate was aspirated in the same session due to interference with knee extension, and the injection was repeated using the novel infrapatellar approach. All patients who received the UGIAI using the infrapatellar approach had the injectates successfully delivered intra-articularly, as confirmed with dynamic ultrasound scanning. Their Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, stiffness, and function scores significantly improved 1 and 4 weeks post-injection. UGIAI of the knee using a novel infrapatellar approach is readily learned and may improve accuracy of UGIAI, even for patients with no effusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".