Publication Patterns of Cancer Cost-Effectiveness Studies Presented at Major Conferences
Bibliographic record
Abstract
OBJECTIVE: To be useful to policymakers and stakeholders, cost-effectiveness analyses (ceas) should be published in a timely manner and without bias. The aims of the present study were to examine the time between conference abstract presentation and subsequent publication, to determine the factors associated with time to publication, to evaluate potential publication bias, and to examine discrepancies in the results between abstract and publication. METHODS: Abstracts of ceas presented at the annual meetings of the American Society of Clinical Oncology (asco), the American Society of Hematology (ash), and the International Society for Pharmacoeconomics and Outcomes Research (ispor) between 1997 and 2007 were reviewed. Time-to-event analysis was performed to assess the timeliness of publication and to examine factors associated with time to publication. Summary statistics were used to assess discrepancies in incremental cost-effectiveness ratios (icers) between abstract and publication. RESULTS: Of 164 abstracts identified, 65 (39.6%) were subsequently published. The 1-, 2-, 3-, and 5-year publication rates were 12.8%, 25%, 34.2%, and 40.5% respectively. Abstracts were more likely to be published if presented at asco than at ispor (hazard ratio: 1.94; p = 0.038). There was no direct evidence of publication bias for abstracts with favourable icers. Comparing icers between abstracts and publications, the mean absolute difference was 23.8%; 50% of studies had a change in icer exceeding 10%. CONCLUSIONS: Publication rates for ceas were low, and publication was not timely with respect to informing the decision-making process for funding. Abstract results often differed from publication results and cannot reliably be used in the decision-making process for funding.
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 | 0.119 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.051 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".