The Dividends of an Effective Clinical Technology Management Program
Bibliographic record
Abstract
In Brief The growth of clinical engineering (CE) departments in the last 20 years has stalled within healthcare facilities despite the fact that the inventory of medical devices within all facilities has grown substantially during this interval. In many organizations, CE’s role is only to maintain the organization’s clinical equipment. The developmental strategy used by CE at the Children’s Hospital of Eastern Ontario has reversed this trend at this facility. This was accomplished by taking on the role of the health technology manager for the institution. Clinical engineering has, since 2001, coordinated the long-range planning function for capital equipment at Children’s Hospital of Eastern Ontario. Since taking on this role, CE has also been assigned the management of annual corporate capital equipment procurement process and coordination of the ongoing implementation of new clinical equipment. The growth of clinical engineering (CE) departments in the last 20 years has stalled within healthcare facilities despite the fact that the inventory of medical devices within all facilities has grown substantially during this interval. In many organizations, CE’s role is only to maintain the organization’s clinical equipment.
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 | 0.055 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".