Regional Prioritization and Replacement of Surgical Tables
Bibliographic record
Abstract
A surgical table audit conducted within the Winnipeg Regional Health Authority (WRHA) revealed one-third of the surgical tables were in need of replacement. Tables within the region requiring replacement were prioritized using a prioritization system developed in-house. A limited amount of capital was allocated to the initiative with the intention of targeting upwards of twenty-five surgical tables for replacement. The option of purchasing remanufactured tables was researched and analyzed to determine its viability compared to purchasing brand new. Research and analysis took the form of literature reviews, discussion with technicians from various hospital sites, along with a site-visit to a remanufacturing plant specializing in the remanufacturing process of surgical tables. The region concluded that the remanufacturing process was an acceptable and cost-effective option for replacement of the tables. Next, a regional RFP was developed. The RFP consisted of standard schedules, technical questions on the remanufacturing process along with a modified pricing schedule. The region was interested in obtaining proposals for three different options; (1) restoration of existing in-house tables, (2) purchase of remanufactured tables, and (3) purchase of new tables. The RFP was developed to allow for the region to select each table from option 1, 2 or 3. Tables were selected according to site-specific needs with efforts focused on maintaining a standardized fleet at each site. A common requirement was the need for higher weight capacity. The strategy, cost effectiveness, and evaluation process used to successfully conduct the regional replacement of surgical tables will be discussed.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".