PP27 Additional Capabilities In Health Technology Assessment To Support Decision Making
Bibliographic record
Abstract
Introduction Decision-making regarding an open or a closed fluid waste management system (FWMS) in the planning of thirty operating rooms (ORs) of a new hospital at the CHU de Québec-Université-Laval was an opportunity to explore additional capabilities in health technology assessment (HTA) to support evidence-based planning. Methods Issues related to FWMSs in ORs were assessed from multiple data sources including: (i) systematic review in indexed databased and grey literature, (ii) waste management laws and regulations, (iii) local registry of reported incidents/accidents, (iv) occupational health and safety database, (v) electronic patient records (EPRs), (vi) field evaluation of two closed FWMSs, (vii) costs, and (viii) survey on FWMSs in ORs of other Quebec hospitals. Results Closed FWMSs in ORs could reduce health care professional exposure to blood and body fluids (BBF) according to two low-quality studies. Cases of occupational and patient exposure to BBF with closed FWMSs, some of which had severe issues, were reported to the U.S. Food and Drug Administration. Depending on the volume, discharge of BBF to the sanitary sewer may be authorized upon the approval of the competent municipal authorities. Compared to an open system, a closed FWMS has the potential to reduce manipulation of canisters during the cases because of large canister capacity (24 L). However, local data showed that BBF and irrigation fluid amounts in ORs are <2 L in 84 percent of cases and >2 L in a minority of surgeries, whereas a closed FWMS is associated with higher costs for BBF volumes <12 L. Other issues were observed during field evaluation (e.g., occupational noise). Closed FWMS implementation in other hospitals was very limited in the survey. Conclusions Available evidence does not support the widespread use of a closed FWMS. Use of mixed-methods in this particular HTA allowed to assist decision makers on the choice of an FWMS in the OR planning.
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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 | 0.101 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".