PedsQL™ and EuroQol Most Responsive Measures To Change in Health-Related Quality of Life in Children with Hodgkin’s Disease.
Bibliographic record
Abstract
Abstract We evaluated four different health related quality of life measures to determine their ability to detect change over time. The four measures included the Health Utilities Index Mark 2 and 3 (HUI 2/3), the PedsQLTM 4.0 Generic Core and Cancer Module, the EuroQol and the Lansky Play - Performance Scale. Children with Hodgkin’s disease, their parents and the clinic nurse were all asked to complete the four measures at four time points: 2 weeks after the 1st course of chemotherapy, on the 3rd day of the 2nd course of chemotherapy, during the 3rd week of radiation and 1 year after diagnosis. At each follow up time point the respondents were asked to indicate whether the child’s HRQL had improved, stayed the same or became worse since the last measurement. 51 adolescents from 12 centres across Canada were enrolled in the study between May 1, 2002 and March 31, 2005. Two patients were excluded: one patient died shortly after the first time point, and the other patient failed to complete any of the questionnaires. The 49 patients included in the analysis had an average age of 14.7 years (8.9 - 17.9), with the two most common stages being IIA (39%) and IVA (18%). Complete data was available on 92% of patients at time 2, 89% at time 3 and 76% at time 4. Four of the 36 patients with complete follow up had relapsed (11%). The summary scores from the patients responses can be seen in the graph below. Figure Figure All measures showed a significant change between time 1 and time 4 (<0.05). When the change in child scores was analysed between the time points using the child’s self-reported change in HRQL, the PedsQL and the EuroQol showed significant change at all time points. Change in Measures According to Reported Global Rating of Change by Patient Time Period HUI2 HUI3 PedsQL PedsQL cancer EuroQol Lansky Improved 1 to 2, n = 7 0.022 0.011 0.015 0.046 0.001 NS 2 to 3, n = 15 NS NS 0.028 0.039 0.017 NS 3 to 4, n = 20 NS NS 0.011 NS <0.001 <0.001 Same 1 to 2, n = 16 NS NS 0.033 NS NS NS 2 to 3, n = 9 NS NS 0.039 0.006 NS 0.028 3 to 4, n = 1 NS NS NS NS NS NS Worse 1 to 2, n = 6 NS NS NS NS 0.033 NS 2 to 3, n = 0 NS NS NS NS NS NS 3 to 4, n = 1 NS NS NS NS NS NS All of the measures were able to detect change in a diverse group of children with Hodgkin’s Disease. The PedsQL and the EuroQol appeared to be the most sensitive to change.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".