Reporting of endpoints and tracking of failures in randomized trials of radiotherapy or concurrent chemoradiotherapy for locally advanced head and neck squamous cell cancer (LA-HNSCC)
Bibliographic record
Abstract
6072 Background: Due to their anatomical complexity, the interactive effects of multiple treatment modalities, the difficulties in differentiating scar tissues versus residual disease and second cancers versus tumor recurrence, LA-HNSCC represent a challenging disease for the reporting of endpoints and the tracking of failures. Methods: We retrieved all randomized trials that began accrual on or after 1978, and enrolled previously untreated nonmetastatic HNSCC patients receiving primary (chemo)radiotherapy. The reporting of endpoints and the tracking of failures in these trials were analyzed. Failures were defined as events meeting a pre-specified endpoint definition. Results: Forty trials involving 13,892 patients fulfilled our inclusion criteria. A total of 125 endpoints were identified: primary versus secondary = 34:91, survival-based (e.g., overall survival [OS]) versus surrogate (e.g. locoregional control [LRC]) = 47:78. In 6 trials, no primary endpoint was identified. LRC and OS accounted for 70% of primary endpoints. All but one trial reported at least one secondary endpoint, with a median of 2 per trial (range: 0–5), and as many as 17 different types of secondary endpoints were reported. Among 72 endpoints tracking locoregional failures, 21/72 (29%) did not define locoregional failure, while 46/72 (64%) specified the absence of complete response as a failure. Whether salvage surgery or elective node dissection was performed or not was reported in less than half of the trials. Furthermore, it was usually not specified if residual disease found during these procedures would account for failure or not. The means (i.e. clinical and/or radiological examinations) to ascertain failures and the protocol-specified timing to track failures were reported in 41% and 67% of surrogate endpoints, respectively. The tracking of other types of failure beyond the first failure is not reported by any of the trials. The reporting of second cancers was found in 15/40 (38%) trials, whereas the duration of follow-up was quantified in 31/40 (78%) trials. Conclusions: These results demonstrate the vast heterogeneity in endpoint reporting and tracking of failures in clinical trials of LA-HNSCC. No significant financial relationships to disclose.
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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 | 0.350 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".