Bibliographic record
Abstract
This chapter contains sections titled: Introduction What Kinds of Children Receive Palliative Care? Is it Just Children with Cancer? When is the Right Time for the Clinician to Consider Palliative Care for a Child? Does Palliative Care in Children Allow “Curative” Treatments to Be Continued? What Symptoms are Children Likely to Have? How Do I Deal with Developmental Differences in Assessing Symptoms? Are There Any Tools to Help with Symptom Assessment? General Principles for Symptom Management in Children Symptom Patterns Notes about Some Specific Symptoms What Medications Can I Use in Children and What Doses? How Do I Make a Reliable Prognostication for a Child? Does the Primary Care Provider Have a Role in Caring for Children with Complex Conditions? What is the Best Way to Work with Families of Children in Palliative Care? How Do I Advise Parents to Talk to Their Children about the Illness? Should Families Try to Continue Their Routines, or is it Better to Focus on the Ill Child? Is it Helpful to Tell Siblings Everything That is Happening? The Final Stages The Bereaved Family What Kinds of Professionals Provide PPC? Where is PPC Provided (and What Difference Does it Make)? Conclusions
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.009 |
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".