Design and Evaluation of Electronic Briefs of Neonatal Intensive Care Unit in Taleghani Hospital, Tabriz, Iran
Bibliographic record
Abstract
More than 9 million neonatal deaths are reported through out the world each year happening in the early weeks of life most of which relate to developing countries. Thus it is very important to present a better way to keep the infants healthy which could be possible by accessing accurate information at any time required during hospitalization of infants. Therefore the required data should be collected, stored and analyzed before which is best possible by using computer. The main objective of this research is enabling researchers and clinicians quick access to the data of the babies admitted in NICU. This study involves the stage of developing a system design and its implementation following the evaluation of the electronic records which is done in a query form. By defining the neccessary terminology and designing a data model, the database and user interface are developed by using a programing language and data base tools. Finally, the system has been evaluated by user satisfaction showing to be about 85% As a result we suggest the hospitals take serious in buying the suitable technology for the NICU ward along with teaching the staffs how to work with it.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".