Contrasting effects of extended low dose versus standard dose shorter course UFT chemotherapy on microscopic versus macroscopic established tumors: Implications for optimal postoperative adjuvant chemotherapy
Bibliographic record
Abstract
Given such differences as relative tumor burden, the optimal dose and schedule for postoperative adjuvant chemotherapy of microscopic disease might be expected to differ significantly from therapy of advanced higher volume disease. We investigated this hypothesis by determining the optimal dose and schedule of the 5-FU pro-drug, UFT, for treatment of early versus later stage disease models of the Lewis lung carcinoma (LLC). Postoperative adjuvant therapy of early stage disease was modeled by intravenous injection of LLC cells and initiating therapy one day later, thus simulating the presence of micrometastases at the time of surgery. As a model of 'late' stage disease, a LLC fragment was implanted subcutaneously and UFT therapy was initiated when the tumor was firmly established and had grown to >5 mm in size. A number of UFT dosing protocols were evaluated such as short-term (daily, for 7 days) maximum tolerated dosing (MTD), e.g. 31 mg/kg/day, or a much longer-term (e.g., daily, for up to 60 days) repetitive dosing using doses such as 24 mg/kg/day (the MTD) or lower. The long-term consecutive administration of UFT at relatively low minimally toxic dose levels is a superior dosing regimen in the postoperative adjuvant chemotherapy model; in contrast, the short-term higher dose protocols were superior for treatment of more advanced, established cancer. In addition, the efficacy of UFT in an adjuvant setting is more effective when drug administration is continued for longer periods and when treatment is initiated at progressively earlier time points, after disease establishment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".