New Technologies and Surgical Innovation
Bibliographic record
Abstract
There is pressure for surgical departments to introduce new and innovative health technologies in an evidence-based manner while ensuring that they are safe and effective and can be managed with available resources. A local health technology assessment (HTA) program was developed to systematically integrate research evidence with local operational management information and to make recommendations for subsequent decision by the departmental executive committee about whether and under what conditions the technology will be used. The authors present a retrospective analysis of the outcomes of this program as used by the Department of Surgery & Surgical Services in the Calgary Health Region over a 5-year period from December 2005 to December 2010. Of the 68 technologies requested, 15 applications were incomplete and dropped, 12 were approved, 3 were approved for a single case on an urgent/emergent basis, 21 were approved for "clinical audit" for a restricted number of cases with outcomes review, 14 were approved for research use only, and 3 were referred to additional review bodies. Subsequent outcome reports resulted in at least 5 technologies being dropped for failure to perform. Decisions based on local HTA program recommendations were rarely "yes" or "no." Rather, many technologies were given restricted approval with full approval contingent on satisfying certain conditions such as clinical outcomes review, training protocol development, or funding. Thus, innovation could be supported while ensuring safety and effectiveness. This local HTA program can be adapted to a variety of settings and can help bridge the gap between evidence and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".