Understanding the EORTC QLQ-BM22, the module for patients with bone metastases
Bibliographic record
Abstract
Bone metastases are a frequent complication of cancer. With advances in effective systemic treatment and supportive care, the survival of patients with bone metastases has improved substantially. Treatment options for bone metastases have been expanding, as reflected in the latest clinical trials testing newer generations of bisphosphonates and increased use of orthopedic surgery. There is a great need to monitor not only the benefits but also the side effects of these treatments. Any interventions should aim primarily at improving the quality of life (QoL) in this group of terminal patients. The use of validated bone metastases-specific QoL instruments should be promoted for use in both daily practice and clinical trials. In response to the need for a comprehensive bone metastases-specific QoL instrument, which has been lacking until now, we have developed the European Organization for Research and Treatment of Cancer (EORTC) QoL Group Bone Metastases Module (QLQ-BM22) according to the module development guidelines of the EORTC Quality of Life Group (QLG). As bone metastases patients are palliative and often demonstrate rapid deterioration, we will conduct Phase IV testing of the EORTC QLQ-BM22 alongside the EORTC QLQ-C15-PAL palliative core questionnaire to ease patient burden when completing the study assessments. The international validation of the Bone Metastases Module will facilitate measurement of important QoL issues in palliative care trials. Healthcare professionals will be able to reliably follow their patients' QoL, help patients in choosing treatments and assess the cost-effectiveness of the treatments. Bone metastases clinical trial QoL outcomes will be compared across trials through the utilization of a consistent and valid module questionnaire.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".