Real-World Healthcare Resource Use Associated with Recurrent or Metastatic Head and Neck Cancer Patients Care in Portugal—TRACE Study
Bibliographic record
Abstract
Recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) is a challenging disease, requiring personalized management by a multidisciplinary team. The aim of this retrospective multicentric study was to characterize real-world healthcare resource use and patient care for R/M HNSCC in Portugal during the first year after diagnosis. A total of 377 patients ineligible for curative treatment were included, mostly male (92.8%), aged 50-69 years (74.5%), with heavy alcohol (72.7%) or smoking habits (89.3%). Oropharynx (33.2%) and oral cavity (28.7%) were primary tumor locations, with lung metastases being the most common (61.4%). Eligible patients for systemic treatment with palliative intent (80.6%) received up to four treatment lines, with varied regimens. Platinum-based combination chemotherapy dominated first-line treatment (>70%), while single-agent chemotherapy and anti-PD1 immunotherapy were prevalent in later lines. Treatment approaches were uniform across disease stages and primary tumor locations but varied geographically. Treated patients received more multidisciplinary support than those who were ineligible. This study provides the first Portuguese real-world description of R/M HNSCC patient characteristics, treatment patterns, and supportive care during the year after diagnosis, highlighting population heterogeneity and aiming to improve patient management.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".