Bibliographic record
Abstract
Summary Cancer studies frequently employ clinical endpoints for outcome reporting in order to estimate treatment effect sizes. Most often these outcome assessments use time‐to‐event measures in addition to tumour response, toxicity and quality of life (QOL). The Kaplan‐Meier method is often used to estimate the actuarial rate for time‐to‐event measures. Non‐stratified or stratified log‐rank tests are frequently applied assessing the treatment effect among groups. The Cox proportional hazards regression model is commonly used to estimate the hazard ratio between different treatments. Because cancer outcome is often confounded by multiple other outcomes (e.g. various causes of death), competing risks regression models are used to assess the treatment effect. In addition, intermediary endpoints, such as changes in tumour size, tumour‐related chemical markers and tumour metabolism may also assist in evaluating new treatments. Therefore, the ability to accurately and reliably assess the direct antitumour effect of investigational therapies is critical for the optimal conduct of clinical trials. The goal of this chapter is to summarize general principles of cancer outcome reporting and estimation of treatment effect, and response assessment.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".