Improving the quality of abstract reporting for economic analyses in oncology.
Bibliographic record
Abstract
109 Background: Increasing costs of cancer drugs underscore the importance of EA, which convey key information about the relative costs and benefits of new interventions. Although guidelines for abstracts exist for phase I, II, and III oncology trials, similar recommendations for EA are lacking. Our objectives were to 1) identify items considered to be essential for EA abstracts; 2) evaluate the quality of EA abstracts submitted to ASCO, ASH, and ISPOR meetings; and 3) propose guidelines for future reporting. Methods: Health economic experts were surveyed and asked to rate each of 24 possible EA elements on a 5-point Likert scale. A scoring system for abstract quality (0=poor and 100=excellent) was devised based on EA elements with an average expert rating ≥ 3.5. All EA abstracts from ASCO (‘97–‘09), ASH (‘04–‘09) and ISPOR (‘97–‘09) were reviewed and assigned a quality score. Results: Of 99 experts surveyed, 50 (51%) responded. Characteristics of respondents: average age = 53; male = 78%; US / Europe / Canada = 54% / 28% / 18%. A total of 216 abstracts were reviewed: ASCO 53%, ASH 14% and ISPOR 33%. Median quality score was 75 (range 48 to 93), but notable deficiencies were observed. For instance, the cost perspective of the EA was reported in only 61% of abstracts, while the time horizon was described in only 47%. An association was seen between year of presentation and overall quality of abstracts (p=0.001), with those from recent years demonstrating better quality scores. There were also disparities in quality scores among EA of different cancer sites (p=0.005). Conclusions: Quality of EA abstracts for oncology has improved over time, but there is room for improvement. Abstracts may be enhanced using guidelines derived from our survey of experts (see table). [Table: see text]
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 | 0.823 | 0.924 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.048 | 0.042 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".