Introducing a lexicon of terms for pediatric palliative care
Bibliographic record
Abstract
The nascent field of paediatric palliative care (PPC) has evolved significantly over a short period of time, as has its terminology. With an emerging literature of program descriptions and clinical research in PPC, the time has come to establish within Canada a shared vocabulary using a stock of terms thatcarry a particular meaning for those working within the field. A list of such terms is called a ‘lexicon’. Lexicons for PPC have been developed in other countries (1) and for adult populations in Canada (2). PedPalASCNET (A Network for Accessible, Sustainable and Collaborative Research in Pediatric Palliative Care) (3), a Canadian Institutes of Health Research-funded PPCresearch collaborative, developed a Lexicon of Terms in Pediatric Palliative Care through a collaborative, iterative process. Members of a working group surveyed the literature and met in person and via a Web conference throughout a 13-month process. The result is a set of definitions that includes 18 terms of importance in the PPC literature. The Lexicon reflects the terms used in Canada in the care for children with chronic, complex and life-threatening conditions. For example, an ‘advance directive’ is defined as “adocument that records preferences for using or limiting certain medical treatments in order to meet short- and long-term goals of care” (4). The Lexicon will serve clinicians and researchers as a standard for the use of terms in PPC in descriptions of their work. [...]
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".