Fully Automated Clinical Engineering Technical Management System
Bibliographic record
Abstract
This manuscript describes the different phases of developing, implementing, and evaluating a unique fully automated clinical engineering system at the Ministry of Health in Jordan. This covers automating all related technical issues in 29 hospitals, 685 health centers, 332 dental clinics, 348 pediatrics and mother care clinics, and 23 blood banks. Every piece of medical equipment was assigned an identity code that can be recognized through a bar code scanning system, and similarly, all other involved parameters, such as hospitals, personnel, spare parts, workshops, and others, are also coded comprehensively. The system presents a powerful software package designed based on Oracle and implemented using a network covering different locations of the Directorate of Biomedical Engineering (DBE) at the Ministry of Health all over Jordan through Web-based interactive connection. The complete automation system proves to be invaluable tool to manage, control, and report all different parameters concerning the considered clinical engineering system including all medical equipment at minimum cost and time as compared with international systems. It is also the first comprehensive system that can read and report in both Arabic and English languages. The system was evaluated and found to be reliable, effective, and unique compared with internationally available systems. The DBE with this automated clinical engineering system has the ISO 9000/2000 certification.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".