Cuidados de enfermería en paciente con cáncer del cuello uterino en el servicio de hospitalización, Clinica Oncosalud 2019
Bibliographic record
Abstract
One of the most common malignancies in the Peruvian woman is cancer of the \ncervix (CCU), surpassing even breast cancer. Because its incidence, is one of \nthe major public health problems in our country. The appearance of this type of \ncancer is given by multiple factors, mainly: genetics and lifestyle. Generally, this \nneoplasm manifests clinically with abnormal vaginal bleeding, unusual vaginal \ndischarge, and pain during sexual intercourse; and in advanced stages there \nmay be vomiting, edema, weight loss, bowel obstruction, among others. \nCurrently, there are multiple ways to eliminate or control cancer or uterine \ncervix, such as surgery, \nThis academic work will develop a care plan individualized nursing a patient \nadmitted to hospital service of the National Institute of Neoplastic Diseases \n(INEN), diagnosed with cervical cancer and a history of dysplasia or \nintraepithelial neoplastic of the cervix (NIC). \nTherefore, the objective of this case report is to improve the quality of the \npsychological aspects as part of holistic nursing care that should provide \noncology nurses. \nThis study was conducted in three chapters. Chapter I refers to the theoretical \nframework in which the issue of cervical cancer, education and nursing \nintervention will take place. In Chapter II the application of the nursing process, \nwhere the valuation domains, taxonomy NANDA, formulation nursing diagnosis \nand care plan NIC, NOC and supported interventions in nursing theory of Afaf \nIbrahim will be deployed will be held Meleis. And, in Chapter III we mention the \nconclusions and recommendations, references according to Vancouver and \nannexes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".