Care transition from hospital to home: cancer patients’ perspective
Bibliographic record
Abstract
OBJECTIVES: The present database contains information on sociodemographic and clinical data as well as data from the Care Transition Measure (CTM 15-Brazil) of cancer patients undergoing clinical or surgical treatment. Data collection was carried out 7 to 30 days after patients' hospital discharge from June to August 2019. Understanding these data can contribute to improving quality of care transitions and avoiding hospital readmissions. DATA DESCRIPTION: This data set encompasses 213 cancer patients characterized by the follow variables: gender, age range, place of residence, race, marital status, schooling, paid work activity, type of treatment, cancer staging, metastasis, comorbidities, main complaint, main complaint grouped as, continuing medication, diagnosis, diagnosis grouped as, cancer type, year of diagnosis, oncology treatment, first hospitalization, readmission in the last 30 days, number of hospitalizations in the last 30 days, readmission in the last 6 months, number of hospitalizations in the last 6 months, readmission in the last year, number of hospitalizations in the last year and the questions 1-15 from CTM 15-Brazil.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".