Medical Equipment Aging: Part III—An Aging Model for Maintenance and Replacement Plannings
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
The first article of this series demonstrated that some medical equipment exhibits clear, progressive deterioration with age, whereas some others do not. The second article showed that although most equipment remains safely and reliably deployed well over its respective depreciation period and the end-of-life or end-of-support dates declared by the respective manufacturers, some equipment needs to be replaced sooner. Because it is not practical to wait for the collection and analysis of large amounts of data to better plan for maintenance and replacement, a simple, quantitative model for equipment aging is introduced in this article to help improve both types of planning. The model was tested on professionals not directly involved in its formulation and applied to 34 equipment types. The results show this aging model can be used by professionals experienced in medical equipment maintenance and management, with only some basic instructions. Furthermore, it can be used to update the traditional “risk-based criteria” for planned maintenance and turning it into a risk management method fully compliant with the ISO 14971 standard.
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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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