Bibliographic record
Abstract
DOI: 10.1200/JOP.2015.009316; published online ahead of print at jop.ascopubs.org on February 2, 2016. Reply to F. Dayyani et al To the Editor:We thank Dayyani et al for their interest in our recent study evaluating serum tumor marker use in patients with advanced solid tumors. We agree that in certain clinical scenarios, serum tumor markers may be useful tools to monitor patients with metastatic disease. Our findings, however, suggest that there is uncertainty regarding the frequency of use and their role in clinical decisionmaking. It is also important to note that no prospective data havedemonstrated improvedoutcomeswith earlier detection of disease progression through biomarker assessment. We agree that future studies are warranted to evaluate the role of serum tumor–markermonitoring in patients with reliable tumor markers with stable or decreasing values as a strategy to defer more costly radiographic imaging studies. Melissa K. Accordino Columbia University College of Physicians and Surgeons
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".