OP18 A Case Study Of Local Context-Dependent Decision-Making In Health Technology Assessment
Bibliographic record
Abstract
Introduction Antibiotics impregnated calcium sulfate (AI-CaSO4) is an innovative practice to ensure local diffusion of antibiotics especially in the treatment of prosthesis or medical implants infections. A recent introduction of AI-CaSO4 at CHU de Québec-Université Laval (CHU de Québec) was followed by a rapid increase in use and costs. A hospital-based health technology assessment (HTA) was then requested to assess the clinical relevance of AI-CaSO4 in surgical site infection (SSI) management. Methods A systematic review of the effectiveness and adverse effects of AI-CaSO4 was performed in indexed databases and grey literature. The local context analysis included different methodologies: 1) interviews with pharmacists, surgeons and operating room managers, 2) data extraction from electronic patient records (EPR), 3) procurement database on CaSO4, and 4) interdisciplinary working group including orthopedic and vascular surgeons, pharmacists, infectiologists, and hospital managers. Results Available evidence suggest that AI-CaSO4 could contribute in the treatment of osteomyelitis whereas no conclusion can be drawn for other medical indications in both treatment and prevention of SSI. A review of 113 surgical procedures showed that AI-CaSO4 was rapidly adopted after only one year and used for various medical indications in neuromodulation, orthopedic and vascular surgery. Osteomyelitis treatment accounted for less than 3% of cases. AI-CaSO4 was mainly used in prevention of SSI (65%) and surgical revisions (74%). Furthermore, local safety issues were raised by a lack of standardization for the preparation and under recording of antibiotics use with AI-CaSO4. Conclusions The current state of knowledge does not support the widespread use AI-CaSO4 at CHU de Québec. This study highlights the importance of adapting HTA approach to the local context to influence decision-making especially in the context of innovating practice in order to insure the relevance, safety and sustainability of care.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".