Sources of information influencing decision-making in orthopaedic surgery - an international online survey of 1147 orthopaedic surgeons
Bibliographic record
Abstract
BACKGROUND: Manufacturers of implants and materials in the field of orthopaedics use significant amounts of funding to produce informational material to influence the decision-making process of orthopaedic surgeons with regards to choice between novel implants and techniques. It remains unclear how far orthopaedic surgeons are really influenced by the materials supplied by companies or whether other, evidence-based publications have a higher impact on their decision-making. The objective was to evaluate the subjective usefulness and usage of different sources of information upon which orthopaedic surgeons base their decisions when acquiring new implants or techniques. METHODS: We undertook an online survey of 1174 orthopaedic surgeons worldwide (of whom n = 305 were head of their department). The questionnaire included 34 items. Sequences were randomized to reduce possible bias. Questions were closed or semi-open with single or multiple answers. The usage and relevance of different sources of information when learning about and selecting orthopaedic treatments were evaluated. Orthopaedic surgeons and trainees were targeted, and were only allowed to respond once over a period of two weeks. Baseline information included country of workplace, level of experience and orthopaedic subspecialisation. The results were statistically evaluated. RESULTS: Independent scientific proof had the highest influence on decisions for treatment while OEM (Original Equipment Manufacturer) driven activities like newsletters, white papers or workshops had the least impact. Comparison of answers from the three best-represented countries in this study (Germany, UK and USA) showed some significant differences: Scientific literature and congresses are significantly more important in the US than in the UK or Germany, although they are very important in all countries. CONCLUSIONS: Independent and peer-reviewed sources of information are preferred by surgeons when choosing between methods and implants. Manufacturers of medical devices in orthopaedics employ a considerable workforce to inform or influence hospital managers and leading doctors with marketing activities. Our results indicate that it might be far more effective to channel at least some of these funds into peer-reviewed research projects, thereby assuring significantly higher acceptance of the related products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".