The Accuracy of Intra-Articular Needle Placements in Osteoarthritic Knee Patients: An Arthroscopic Assessment.
Bibliographic record
Abstract
Background: The intra-articular of hyaluronic injection is widely used for osteoarthritic knee (knee OA). However, incorrect needle placement may cause discomfort and reduce effectiveness of the treatment. Objective: To assess the accuracy rates of needle placements into the intra-articular space of knee OA. Material and Method: This was a prospective study. Twenty-two patients with knee OA at Rajavithi Hospital received needle placement into intra-articular spaces using the three approaches, anteromedial (AM), anterolateral (AL), and lying lateral mid patella (LMP). The accuracy rates were confirmed by arthroscopy. Before and after injection of intra-articular hyaluronic acid at week 2, the visual analogue scale (VAS) was used to assess pain and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score was used in the evaluation of knee OA. The quality of life (QoL) was measured using the generic instrument Short Form-36 (SF-36). Results: The majority of the participants were female. Their mean age was 58.41±5.82 years old, and their mean (±SD) BMI was 25.07±2.47 kg/m2. Their VAS and WOMAC scores improved significantly after injection compared to the baseline (p<0.001), but no significant differences in their QoL (SF-36) were observed after injection. The accuracy rate of intra-articular needle placement was highest (77.3%) using the LMP, followed by AL (63.6%) and lowest in the AM portal (31.8%). No significant difference was found between the accuracy rates of any of the needle placement groups based on KeL grade II. As for KeL stage III, the only significant difference between the accuracy rates was between those of the AM and the LMP approaches (23.1 vs. 76.9 accuracy rates, p = 0.006). Conclusion: The LMP approach had the highest accuracy rate and is recommended for the treatment of patients with mild to moderate knee OA.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".