Bibliographic record
Abstract
During the COVID-19 pandemic, the Biomedical Engineering team at the University of Ottawa Heart Institute (UOHI) faced challenges coordinating with external medical equipment vendors’ service experts due to travel restrictions. Although the UOHI BME team is highly skilled and capable of addressing many equipment issues in-house, certain specialized or vendor-specific interventions still require external input. With most vendors and their support teams located outside Ottawa, UOHI Biomed explored alternative solutions during the pandemic to ensure timely remote service support, minimize delays, reduce equipment downtime, and control costs. This paper outlines an attempt by the Biomedical Engineering department at UOHI to explore a Remote Service Support Program (RSSP) aimed at improving the process of obtaining assistance from vendors by enabling remote connections between Biomedical Engineering Technologists (BMETs) and medical equipment service experts through live, real-time calls. Although the program offered features such as hands-free maintenance support and the ability for remote experts to share documents or screenshots, it ultimately did not integrate well with the department’s established workflows. Furthermore, although several manufacturers had existing remote support platforms, in practice, BMETs continued to rely on familiar tools such as video calls, phone calls, or general-purpose communication platforms when needed. As a result, the Remote Service Support program was not adopted for sustained use.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.024 |
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".