The PRECISION Survey: Preferences of Physicians Regarding Ultrasound-Guided Intra-Articular Injections
Bibliographic record
Abstract
OBJECTIVE: The objectives of this survey study were to: (1) determine practice patterns, (2) assess beliefs and attitudes toward ultrasound-guided intra-articular injections (UGIIs), (3) identify barriers to the use of UGII, and (4) determine any differences in beliefs and attitudes based on age or specialty. METHODS: A survey was developed using a focus group including physicians who perform intra-articular injections of the knee, shoulder, and/or hip. After validation by the focus group, the final survey (28 questions) was e-mailed to members of the Canadian Academy of Sport and Exercise Medicine (N = 632). RESULTS: A total of 168 responses were received (26.6%). Nearly half of respondents rarely/never had access to UGII equipment (48.5%), and over half did not have adequate training in UGIIs (56.8%-68.8%). About half of respondents agreed that UGII improves accuracy in knee injections (50.9%); only 35.4% agreed there was evidence to support UGII over non-ultrasound-guided intra-articular injections (NGIIs) of the knee. Physicians younger than 50 years were significantly more likely to use UGII for the knee and hip if they had better access to equipment (P < 0.0005 for both); they were more likely to use UGII for the knee if it was less time-consuming (P = 0.001). CONCLUSIONS: The majority of respondents are not using UGII for the knee or shoulder. Physicians may overestimate their accuracy in performing NGIIs. The biggest barriers to UGII were identified as: (1) inadequate training; (2) lack of access to equipment; and (3) lack of time. Younger physicians seem more open to adopting UGII if barriers are addressed.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".