Improving providers' survival estimates and selection of prognosis‐ and guidelines‐appropriate treatment for patients with symptomatic bone metastases: Development of the Bone Metastases Ensemble Trees for Survival Decision Support Platform
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: In the management of symptomatic bone metastases, current practice guidelines do not provide clear methodology for selecting palliative radiotherapy (RT) regimens based on specific patient and disease features. Decision support aids may offer an effective means for translating the complex data needed to render individualised treatment decisions, yet no such tools are available for use in this setting. Thus, we describe the development of the Bone Metastases Ensemble Trees for Survival-Decision Support Platform (BMETS-DSP), which aims to optimise selection of evidence-based, individualised palliative RT regimens. METHOD: The Ottawa Decision Support Framework was used as the theoretical basis for development of BMETS-DSP. First, we utilised stakeholder input and review of the literature to assess determinants underlying the provider decision. Based on this assessment and iterative stakeholder feedback, we developed the web-based, provider-facing BMETS-DSP. Consistent with the underlying theoretical framework, our design also included assessment of decision quality using the International Patient Decision Aids Standards (IPDAS) certification checklist. RESULTS: Stakeholder input and review of 54 evidence-based publications identified the following determinants of the provider decision: estimated prognosis, characteristics of the target symptomatic lesion and the primary cancer type, consideration of alternative interventions, access to patient-specific recommendations, and patient preferences. Based on these determinants, we developed the BMETS-DSP that (1) collects patient-specific data, (2) displays an individualised predicted survival curve, and (3) provides case-specific, evidence-based recommendations regarding RT, open surgery, systemic therapy, and hospice referral to aid in the decision-making process. The finalised tool met IPDAS quality requirements. Preliminary results of a pilot assessment suggest impact of clinical outcomes. CONCLUSIONS: We describe the successful development of a provider-facing decision support platform to aid in the provision of palliative RT in better alignment with patient and disease features. Impact of the BMETS-DSP on decision outcomes will be further assessed in a randomised, controlled study.
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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 | 0.053 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".