Medical Equipment Aging
Bibliographic record
Abstract
As demonstrated in the first article of this series, some medical equipment types exhibit clear, progressive deterioration with age, whereas some others do not. Regardless of its aging behavior, equipment will eventually be replaced and disposed of for a variety of reasons—eg, its repair cost has become unjustifiable, it is no longer possible to repair, or its usefulness has been superseded by newer technologies. Although it is not possible to pinpoint the exact cause of each replacement or disposal, it is useful to understand when such action takes place during the equipment’s lifespan because this can help healthcare delivery organizations to better plan and conserve its capital resources while satisfying the needs and desires of their staff to provide safe and high-quality care. The results of an analysis of the disposal pattern of ~340 thousand pieces of equipment in the period of 30+ years show age is not the primary determinant for replacement or disposal. Most equipment is deployed well past the respective depreciation period and the end-of-life or end-of-support dates declared by their respective manufacturers, without significant negative impacts on patient care. In fact, the life expectancies estimated from the disposal data are typically double of American Hospital Association’s estimated useful lives. Such accomplishment is a testimony of the extraordinary efforts made by clinical engineering/healthcare technology management professionals in keeping equipment safe and reliable in the most cost-effective manner for a very long time.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".