Improving the Quality of Abstract Reporting for Economic Analyses in Oncology
Bibliographic record
Abstract
BACKGROUND: The increasing cost of cancer drugs underscores the importance of economic analyses. Although guidelines for abstract reporting of randomized controlled studies and phase i trials are available, similar recommendations for conference abstracts of economic analyses are lacking. Our objectives were to identify items considered to be essential in abstracts of economic analyses;to evaluate the quality of abstracts submitted to the American Society of Clinical Oncology (asco), the American Society of Hematology (ash), and the International Society for Pharmacoeconomics and Outcomes Research (ispor) meetings; andto propose guidelines for future abstract reporting at conferences. METHODS: Health economic experts were surveyed and asked to rate each of 24 possible abstract elements on a 5-point Likert scale. A scoring system for abstract quality was devised based on elements with an average expert rating of 3.5 or greater. Abstracts for economic analyses from asco, ash, and ispor meetings were reviewed and assigned a quality score. RESULTS: Of 99 experts, 50 (51%) responded to the survey (average age: 53 years; 78% men; 54% from the United States, 28% from Europe, 18% from Canada). In total, 216 abstracts were reviewed: asco, 53%; ash, 14%; and ispor, 33%. The median quality score was 75, but notable deficiencies were observed. Cost perspective was reported in only 61% of abstracts, and time horizon was described in only 47%. Abstracts from recent years demonstrated better quality scores. We also observed disparities in quality scores for various cancer sites (p = 0.005). CONCLUSIONS: The quality of conference abstracts for economic analyses in oncology has room for improvement. Abstracts may be enhanced using the guidelines derived from our survey of experts.
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 | 0.793 | 0.927 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.045 | 0.035 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".