Attitudes and Knowledge Regarding Adipose-Derived Stem Cell Therapy: A Survey of Canadian Orthopedic Surgeons
Bibliographic record
Abstract
Background Adipose-derived stem cells (ADSCs) hold therapeutic potential for the treatment of orthopedic conditions. However, no surveys to date have evaluated physicians' awareness and receptiveness toward these therapies - an essential step in assessing the Canadian healthcare system's readiness for ADSC implementation, alongside regulatory and technical considerations. We hypothesized a generally low level of exposure to this therapeutic modality, with higher levels of awareness and interest expected among respondents specializing in sports medicine and reconstruction. Methods An eight-item questionnaire was distributed to members of the Arthroscopic Association of Canada and the Orthopedic Division at (redacted for blinding) University. Statistical analysis was conducted using mixed-effects logistic regression. Results The survey achieved a 12.9% response rate, yielding 50 responses. Most respondents (62%) reported hearing about ADSCs only a few times per year, primarily through scientific journals and colleagues. Only 10% expressed interest in incorporating ADSCs into their future practice. Common barriers included insufficient evidence, high costs, and regulatory limitations. No significant association was found between orthopedic subspecialty and receptiveness to ADSC therapy. Conclusions Canadian orthopedic surgeons show limited awareness of, and willingness to adopt, ADSC therapy in clinical practice. Contrary to our hypothesis, no significant differences were observed between subspecialties. Future studies should aim for larger, more representative samples to support more robust conclusions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".