Induction Chemotherapy with Docetaxel, Cisplatin and 5-Fluorouracil (TPF) in Locally Advanced Head and Neck Cancer: An Asian Single Institute Experience
Bibliographic record
Abstract
Background: A retrospective analysis of treating locally advanced head and neck cancer with induction chemotherapy with docetaxel plus cisplatin and 5-fluorouracil (TPF) among Taiwan patients. The purpose of this study was to report response rates, survival, and toxicity of induction chemotherapy with TPF among Asians.Methods: Thirty-three patients with stage III-IV head and neck cancer with good performance status (ECOG:0-2) were enrolled. Docetaxel 60 mg/m^2, cisplatin 75 mg/m^2, and 5-fluourocil 850 mg/m^2 for 4 days were administered every 3 weeks to, at the most, 3 cycles. The Kaplan-Meier survival curve was used to assess survival rates. Response rates and toxicities were graded by RECIST version 1.1 and the expanded common toxicity criteria of the Clinical Trials Group of the National Cancer Institute of Canada.Results: The complete response rate after induction of TPF was 9.1%, partial response rate was 66.7%, and the rate of stable disease was 9.1%, while the rate of progressive disease was 9.1%. The one-year overall survival rate was 93%, and one-year progression-free survival rate was 71.4%. Hematologic toxicities greater than grade 3 were noted in a total of 20 patients (60.6%). Seven cases among the twenty neutropenia patients developed febrile neutropenia. Three patients had acute renal failure.Conclusions: This TPF regimen is effective in Asian patients as induction chemotherapy for locally advanced squamous cell carcinoma of head and neck. However, febrile neutropenia remains the most important consideration. Avoiding use in the elderly aged more than 70 years of age or early preventive GCSF to keep total WBC ＞2000/uL be helpful.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".