Evaluating the Impact of Radiologic Procedures on Nursing Care Planning and Patient Recovery Outcomes
Bibliographic record
Abstract
Background: Radiologic procedures are essential in today's healthcare system, affecting the whole process from diagnosis through treatment to recovery. The radiologic competency of the hospital, the risk management strategy, and the patient education will all depend on the imaging results. Hence, it is a must to know the radiology-nursing interaction in order to increase safety, improve communication and recovery outcomes. Objective: The review was intended to highlight the current practices, challenges, and opportunities for improvement in clinical and patient-centered outcomes by evaluating the impact of diagnostic as well as interventional radiologic procedures on nursing care planning and patient recovery. Methods: The authors performed a narrative literature review using PubMed and Google Scholar to locate peer-reviewed articles that were published within the time frame of January 2020 to September 2024. The search was done by employing the combination of Medical Subject Headings (MeSH) and free-text keywords related to radiologic procedures, nursing care, and patient recovery. The articles that were chosen for inclusion in the review were the ones that had provided quantitative, qualitative, or mixed-methods research, systematic reviews, and clinical guidelines centered on nursing roles in imaging, interdisciplinary coordination, and recovery outcomes. Results: The data extracted resulted in the identification of five main themes: 1) clinical nursing roles in pre- and post-procedure care, 2) interventional radiology and patient monitoring, 3) communication and education strategies, 4) technological integration, including informatics and imaging analytics, and 5) space barriers of limited training, ethical concerns, and communication gaps. It is a common viewpoint that the coordination of radiologic-nursing practices leads to an increase in the safety, satisfaction, and recovery of patients, but the total implementation across all hospitals is still in progress. Conclusion: Radiologic procedures are vital not only in the nursing care process but also in the recovery outcomes. To maximize the impact of radiology on nursing, comprehensive training, interdisciplinary coordination, and consistent hospital policies across departments are few of the ways
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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 | 0.043 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".