Paediatric health-related quality of life: what is it and why should we measure it?
Bibliographic record
Abstract
As a paediatrician, you follow a number of children with chronic health conditions in your practice. You provide them with a variety of therapies and would like to know whether your treatments are having an impact, in particular whether there has been a change in the patient's health-related quality of life (HRQOL). HRQOL measures have the potential to augment the information that clinicians have available, to enhance their clinical decisions and assess the impact of a chronic health condition on a child's life. How should you try to capture this information? ### What is health-related quality of life? The WHO defines quality of life (QOL) as ‘a child's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns’1 and HRQOL) as ‘a child's goals, expectations, standards or concerns about their overall health and health-related domains’.1 ,2 That being said, many other definitions of HRQOL have been proposed over the years, and a variety of terms are currently used.3 ,4 Although the term QOL is sometimes used interchangeably with HRQOL, QOL is actually a broader construct that encompasses aspects of life that may not be amenable to healthcare services.5 Thus, spirituality and financial resources are, for example, often included in QOL, but are not necessarily included as part of HRQOL. In this paper, we regard QOL in children as a multidimensional subjective concept that includes social, emotional, cognitive and physical functioning as well as cultural aspects of the child and family, while HRQOL incorporates measures of physical symptoms, functional status and disease impact on psychological and social functioning.6 ,7 Children growing up with chronic health conditions (or suffered a severe acute illness and experience late effects) are at greater risk for …
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".