Regionalization of Health Care in Head and Neck Cancer: Concept and Considerations
Bibliographic record
Abstract
Introduction: Head and neck cancer are rare and require complex medical and surgical management. Regionalization or centralization of care, defined as the concentration of patients with complex diseases from a specific area in institutions with more experienced and highly functional multidisciplinary teams, may be an alternative to achieve better oncologic outcomes. Objective: To systematize the current knowledge regarding the centralization of care in head and neck oncology and its consequences in the practice of related surgeries. Data Synthesis: Currently, there is evidence that this strategy shows better oncologic outcomes in centers with greater volumes, greater adherence to evidence-based clinical guidelines and quality indicators, and a multidisciplinary team in charge of decision-making. The center in Ontario, Canada, is framed as an example of this strategy, achieving improved outcomes while maintaining a high level of quality. Conclusion: Although more high-quality studies are needed to support this strategy, we believe that the evidence already available is sufficient to consider it a valid option to improve the oncologic outcomes of patients.
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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 | 0.032 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".