A tailored approach to horizon scanning for cancer medicines
Bibliographic record
Abstract
BACKGROUND: Horizon scanning (HS) is the systematic identification of emerging therapies to inform policy and decision-makers. We developed an agile and tailored HS methodology that combined multi-criteria decision analysis weighting and Delphi rounds. As secondary objectives, we aimed to identify new medicines in melanoma, non-small cell lung cancer and colorectal cancer most likely to impact the Australian government's pharmaceutical budget by 2025 and to compare clinician and consumer priorities in cancer medicine reimbursement. METHOD: Three cancer-specific clinician panels (total n = 27) and a consumer panel (n = 7) were formed. Six prioritisation criteria were developed with consumer input. Criteria weightings were elicited using the Analytic Hierarchy Process (AHP). Candidate medicines were identified and filtered from a primary database and validated against secondary and tertiary sources. Clinician panels participated in a three-round Delphi survey to identify and score the top five medicines in each cancer type. RESULTS: The AHP and Delphi process was completed in eight weeks. Prioritisation criteria focused on toxicity, quality of life (QoL), cost savings, strength of evidence, survival, and unmet need. In both curative and non-curative settings, consumers prioritised toxicity and QoL over survival gains, whereas clinicians prioritised survival. HS results project the ongoing prevalence of high-cost medicines. Since completion in October 2021, the HS has identified 70 % of relevant medicines submitted for Pharmaceutical Benefit Advisory Committee assessment and 60% of the medicines that received a positive recommendation. CONCLUSION: Tested in the Australian context, our method appears to be an efficient and flexible approach to HS that can be tailored to address specific disease types by using elicited weights to prioritise according to incremental value from both a consumer and clinical perspective. POLICY SUMMARY: Since HS is of global interest, our example provides a reproducible blueprint for adaptation to other healthcare settings that integrates consumer input and priorities.
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 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 | 0.046 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".