Bibliographic record
Abstract
Background and aims: It is a western cultural expectation that the death of a child is rare. However, with increasing technology, children with complex conditions are surviving longer. Aims: There is a great need to improve how we care for dying children and their families, specifically around the interventions a family wishes to be included in their child’s end-of-life care. Inconsistencies in or absence of specifics of the family’s wishes in the physician’s notes combined with a lack of a clear Do Not Attempt Resuscitation (DNAR) order has resulted in situations where teams and/or families are uncertain how to proceed when the status of a dying child shifts. Methods: A literature review revealed that while the adult world has been successful in designing clear DNAR orders specific to the patient’s and family’s wishes, few tools are available for planning end-of-life interventions in pediatrics. Our interdisciplinary Pediatric Intensive Care Unit’s End-of-Life Care Committee developed, tested, and trialed a Levels of Intervention DNAR form over two years. Results: Following feedback and modifications, it is now used hospital-wide, allowing clear guidance for teams and families when discussing pediatric end-of-life care directives. Conclusions: The ability to delineate specific interventions, rather than a blanket DNAR or ‘do everything’ alleviates parental anxiety and allows appropriate discussions around interventions provided to children with life-limiting diseases, supporting staff and families providing quality end-of-life care for children.Table: No title available.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.644 | 0.510 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".