Pediatric Oncology Clinic Care Model: Achieving Better Continuity of Care for Patients in a Medium-sized Program
Bibliographic record
Abstract
Providing the best care in both the inpatient and outpatient settings to pediatric oncology patients is all programs goal. Using continuous improvement methodologies, we changed from a solely team-based physician care model to a hybrid model. All patients were assigned a dedicated oncologist. There would then be 2 types of weeks of outpatient clinical service. A "Doc of the Day" week where each oncologist would have a specific day in clinic when their assigned patients would be scheduled, and then a "Doc of the Week" week where one physician would cover clinic for the week. Patient satisfaction surveys done before and 14 months after changing the model of care showed that patients were very satisfied with the care they received in both models. A questionnaire to staff 14 months after changing showed that the biggest effect was increased continuity of care, followed by more efficient clinic workflow and increased consistency of care. Staff felt it provided better planning and delivery of care. A hybrid model of care with a primary physician for each patient and assigned clinic days, alternating with weeks of single physician coverage is a feasible model of care for a medium-sized pediatric oncology program.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".