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Record W4413357252 · doi:10.1055/s-0045-1802574

Regionalization of Health Care in Head and Neck Cancer: Concept and Considerations

2025· article· en· W4413357252 on OpenAlexaboutno aff
Sebastián Castro, Mario Tapia, Felipe Cardemil

Bibliographic record

VenueInternational Archives of Otorhinolaryngology · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
FundersFondo Nacional de Desarrollo Científico y TecnológicoAgencia Nacional de Investigación y Desarrollo
KeywordsMedicineMultidisciplinary approachHead and neckHead and neck cancerMultidisciplinary teamQuality (philosophy)Health careCancerFamily medicineIntensive care medicineMedical physicsNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.341
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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