Evaluation of Radiation Dose Received by Premature Neonates Admitted to Neonatal Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate the radiation dose received by premature neonates using diagnostic radiographies. METHODS: This cross-sectional study was conducted on 116 premature neonates with gestational age from 25 to 37 weeks; with the diagnosis of neonatal respiratory distress syndrome (NRDS) and tachypnea, they were admitted to a neonatal intensive care unit (NICU) at Ahvaz Imam Khomeini Hospital in 2015. For assessing the dose received, the model GR-200 thermoluminescent dosimeter (TLD) was used. For each premature neonate under radiation, three TLDs separately (one for each) were placed on surfaces of Ch1, T1, and G1 (chest, thyroid, and gonad of first newborn, respectively). Moreover, for the adjacent neonate at a distance of 60 - 100 cm, two TLDs were laid in the surfaces of T2 and G2 (thyroid and gonad of second newborn, respectively). The dose received by TLDs for any baby and the adjacent neonate under the entrance surface dose (ESD) was estimated. RESULTS: The mean of neonates' weight under study was 1,950.78 ± 484.9 g. During the hospitalization period, minimum one and maximum three radiographies were done for any premature neonate. The doses received in the premature neonates to Ch1, T1 and G1 were 0.08 ± 0.01, 0.06 ± 0.01, and 0.05 ± 0.01 mSv, respectively and for adjacent infants for T2 and G2 were 0.003 ± 0.001 and 0.002 ± 0.0009 mSv, respectively. CONCLUSIONS: In the study, radiation dose received by organs at risk of premature neonates was lower than the international criteria and standards, therefore, also due to the lack of radiation damage threshold, to limit collimator, and the use of the proper filtration, kilovoltage and time during radiography of premature neonates are recommended.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".