Bibliographic record
Abstract
At least 20 million children worldwide would benefit from Pediatric Palliative Care (PPC) annually, and eight million children would need specialized PPC services. In the USA alone, more than 42,000 children, 0–19 years, die annually; fifty-five percent of them are infants younger than one year old. Interdisciplinary PPC is about matching treatment to patient goals and is considered specialized medical care for children with a serious illness. It is focused on relieving pain, distressing symptoms, and stress from a serious illness and is appropriate at any age and at any stage, together with curative treatment. The primary PPC goal is to improve the quality of life both for the child and for his/her family. Sadly, advances in the control of symptoms in children dying of life-limiting diseases have often not kept pace with treatment directed at curing the underlying disease. Data reveal that the majority of distressing symptoms in children with an advanced serious illness (such as pain, dyspnea and nausea/vomiting) are not treated, and, when treated, therapy is commonly ineffective. Emerging evidence shows that palliative care involvement results in improved quality of life, as well as prolongation of life. High-quality pediatric palliative care for children with serious illnesses is now an expected standard of medical care. However, there still remain significant barriers to achieving optimal care, related to lack of formal education, reimbursement issues, the emotional impact of caring for a dying child, and most importantly, the lack of interdisciplinary PPC teams with sufficient staffing. Fortunately, considerable advances have been made in recent years providing PPC around the globe both in resource-poor and resource-rich countries through care provided at children’s hospitals, outpatient palliative care clinics, palliative home care, and free-standing children’s hospice houses. This book, authored by leading authorities in the field, is dedicated to describing existing gaps, as well as the achievements made in clinical care, education, training, and research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.022 |
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".