Reporting of Time-to-Event End Points and Tracking of Failures in Randomized Trials of Radiotherapy With or Without Any Concomitant Anticancer Agent for Locally Advanced Head and Neck Cancer
Bibliographic record
Abstract
PURPOSE: For multiple reasons, including complexities in anatomy and management, locally advanced squamous cell carcinomas of the head and neck (SCCHNs) represent a challenging disease for the reporting of end points and the tracking of failures. METHODS: We retrieved all randomized trials published in English that began accrual on or after 1978 and enrolled previously untreated patients with nonmetastatic SCCHN receiving primary radiotherapy with or without any concomitant anticancer agent. The reporting of time-to-event end points and the tracking of failures in these trials were analyzed. Failures were defined as events meeting a prespecified end point definition. RESULTS: Forty trials involving a total of 125 time-to-event end points were identified. A total of 17 different types of end points were reported. Locoregional control and overall survival accounted for 70% of primary end points. Except for survival, the definitions used for all other end points were heterogeneous. Among 72 end points tracking locoregional failures, 29% did not define the term, whereas 64% specified the absence of complete response as a failure. Overall, the specification of details related to elective neck dissection or salvage surgery to define locoregional failures was deficient. Furthermore, it was rarely stated whether residual disease found during these procedures represents a failure. The methods and timing specifications to assess failures were frequently missing in published reports. The tracking of other types of failure beyond the first failure was reported in only one trial. CONCLUSION: These results support the need to standardize the selection, definition, and reporting of time-to-event end points in clinical trials of locally advanced SCCHN.
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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 | 0.359 | 0.577 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".