Hyperbaric oxygen therapy and diabetic foot ulcers: knowledge and attitudes of Canadian primary care physicians.
Bibliographic record
Abstract
OBJECTIVE: To explore physicians' knowledge of and attitudes toward hyperbaric oxygen therapy (HBOT) in order to better understand current diabetic foot ulcer management practices and to determine potential barriers to HBOT use. DESIGN: A 24-item questionnaire. SETTING: Primary Care Today conference in Toronto, Ont, in May of 2006. PARTICIPANTS: Physician attendees, 313 of whom completed the survey. MAIN OUTCOME MEASURES: Self-reported knowledge of and attitudes toward HBOT. RESULTS: Less than 10% of respondents had a good knowledge of HBOT, but 57% had a good attitude toward HBOT. Knowledge of and attitude toward HBOT were positively correlated (P < .0001). Good knowledge of HBOT was associated with sex (P = .0334), age younger than 40 years (P = .0803), years in medical practice (P = .0646), patient requests for HBOT referrals (P = .0127), and having previously referred patients for HBOT (P < .001). Twenty years or more in medical practice (P = .0593) and receiving patient requests for HBOT (P = .0394) were multivariate predictors of having good knowledge of HBOT. Good attitude toward HBOT was associated with age younger than 40 years (P = .0613) and having previously referred patients for HBOT (P = .0013). Multivariate analysis showed that male physicians (P = .0026) received more patient requests for HBOT (P < .0001), had good knowledge (P = .0129) and a good attitude (P = .0488), and were more likely to refer patients for HBOT. CONCLUSION: Primary care physicians have underdeveloped knowledge of HBOT, but their generally positive attitudes toward its use suggest that they might be receptive to educational interventions. Educating both physicians and patients about HBOT, specifically its cost-effectiveness, might encourage future use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".