The Paediatric Palliative Screening Scale: Further validity testing
Bibliographic record
Abstract
BACKGROUND: Paediatric palliative care is still often introduced late in the illness trajectory of children with life-limiting diseases. Translating palliative care into practice continues to be a challenge. AIM: To validate the Paediatric Palliative Screening Scale further by defining attributes that predict the need for palliative care in children between 1 and 18 years. DESIGN: Proportional-odds logistic regression analysis was performed to investigate the relationship between the attributes of the Paediatric Palliative Screening Scale and the experts' assessment of case vignettes with various combinations of different attribute characteristics. Estimates from regression analysis were transformed to empirical weightings of the Paediatric Palliative Screening Scale attribute characteristics. SETTING/PARTICIPANTS: Online questionnaires with case vignettes were sent to 33 paediatric palliative care experts from Europe, the United States, Canada, Australia and New Zealand. RESULTS: The highest weightings among the five previously defined attributes were estimated life expectancy <12 months (40% of maximum score) and preferences of the child/parents received (24%). Trajectory of disease and impact on daily activities of the child, expected outcome of treatment directed at the disease and burden of treatment, and symptom or problem burden were weighted less. CONCLUSIONS: According to this second step of psychometric testing of the Paediatric Palliative Screening Scale, the strongest and most urgent necessity indicators for a palliative care approach are life expectancy and child/family preferences. These results are somewhat discrepant with results from the previous validation of the instrument as well as previous research findings.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".