Efficacy endpoints (EEPs) in melanoma randomized controlled adjuvant trials (RCATs).
Bibliographic record
Abstract
e13049 Background: Inconsistencies in RCAT EEPs definitions and reporting complicate the evaluation of EEPs as adequate surrogates for overall survival (OS) and the comparison of the EEPs between trials. We aimed to identify issues in reporting and defining of EEPs used in melanoma RCATs. Methods: To identify phase 3 melanoma RCATs published in English language journals from 1994- 2011, a Pubmed database search and systematic review of published meta-analyses were performed. RCAT characteristics and endpoint definitions were obtained. Original protocols were requested. Results: A total of 34 RCATs met the criteria for review. Interferon alone or in combination with another agent was the most commonly tested treatment. Node positive disease was included in 24 studies with 1 including resected metastatic disease. The eligibility criteria in regards to age, previous treatment, and performance status were not reported in 26%, 32%, and 53% of studies respectively. The baseline and follow-up radiological tests were not specified in 35% and 26% of studies respectively. Eight time-to-event (TTE) EEPs were identified, with most studies using more than one (See Table). The primary EEP was not specified 23% of the time. Clear definitions of TTE EEPs start and end time criteria were absent in 58% and 44% of studies, respectively. Conclusions: The inconsistencies in the reporting of EEPs in melanoma RCATs is due to a lack of standardization in their definition and measurement. Standardizing EEPs will reduce resource consumption and enhance the comparison of agents across studies in melanoma RCATs. [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 | 0.213 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".