ICA-EMA: A Tool for Assessing Nursing Complexity of Patients with Oncohematologic Disease in an Italian Center
Bibliographic record
Abstract
Inpatients with hematologic cancer (HC), particularly in an aging population, often require levels of nursing care that feel very demanding. Therefore, it is important to assess nursing complexity in this care environment. The purpose of this study is to assess nursing complexity of inpatients with HC. A prospective observational study was carried out on 131 patients admitted to an adult hematologic center in northern Italy. The following variables were analyzed by means of the Index of Caring Complexity (ICA): age, sex, diagnostic category, purpose of admission, presence of transplant, Charlson Comorbidity Index, and length of stay. A total sample of 131 patients were enrolled. Patients older than 65 years, with a history of transplant, admitted for complications, and with a diagnosis of myeloma or myelodysplasia had higher ICA scores. Therefore, patients in these groups are more likely to exhibit a higher nursing complexity than other patients. The study results can help health-care professionals identify, at an early stage, patients who need higher levels of nursing care; promote a more efficient allocation of nursing staff to the patient needs based on their group; and qualify the need for higher levels of nursing care in order to improve nursing care quality and achieve higher standards of care in Italian hematologic centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".