How Are Decisions to Introduce New Surgical Technologies Made? Advanced Laparoscopic Surgery at a Canadian Community Hospital: A Qualitative Case Study and Evaluation
Bibliographic record
Abstract
The introduction of many new surgical technologies is associated with increased costs and uncertainty regarding risks and benefits. Currently, little is known about how decisions are made regarding the adoption of surgical innovations. To study the decision-making process for adoption of advanced laparoscopic surgical procedures at a community hospital in Toronto, Canada, we used qualitative case study methods. Data were collected using semi-structured interviews with key informants. We performed a modified thematic analysis of the data, using the conceptual framework for priority setting known as accountability for reasonableness, which consists of 4 conditions: relevance, publicity, appeals, and enforcement. Several advanced laparoscopic surgical procedures were introduced at the hospital between 2000 and 2005. During that time, there was no structured, explicit process for making decisions about introducing new surgical technologies. Use of the new surgical technologies was relevant, as measured by the perception of patient benefit and alignment with the hospital's strategic priorities. There was no systematic structure in place to oversee publicity, appeals, or enforcement. The decision to adopt advanced laparoscopic surgery at a community hospital in Toronto, Canada, was made primarily on the basis of its relevance to patient care. The process for making decisions about the adoption of new surgical technologies can be improved.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".