Defining the BC Provincial Preventive Maintenance Program: World Health Organization Device Type Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is no slowing down the perennial increase of in-hospital medical devices, which presents an ongoing challenge to BME departments with limited resources available for service demands. A computerized maintenance management system (CMMS) is a prerequisite for the execution and sustainment of a successful preventive maintenance (PM) program. Implementing a provincial database revealed that medical device inspections, PM schedules, and job procedures varied widely between provincial facilities. This paper will focus on the successful implementation of a Provincial PM program in British Columbia and the historical context to arrive at this point. It will also describe the risk and frequency of device types that constitute the PM schedule using the World Health Organizations “Medical Equipment Maintenance Programme” methodology. Over the course of 20 months, the British Columbia Biomedical Engineering (BCBME) CMMS team classified over 1,000 medical device types. Overall, the BCMBE program reduced the total number of device types for both Critical and Normal devices, and increased the number of Not Scheduled devices and improved the efficiency and efficacy of our PM program. It is our hope that others will find value in our approach and its implementation at a provincial level.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it