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Record W2905176546 · doi:10.1002/hed.25443

AHNS series: Do you know your guidelines? Guideline recommendations for recurrent and persistent head and neck cancer after primary treatment

2018· article· en· W2905176546 on OpenAlexaff
Ryan McSpadden, Chad Zender, Antoine Eskander

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsToronto East General HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineHead and neck cancerGuidelinePrimary treatmentSurgeryPrimary tumorHead and neckCancerRadiation therapyRadiologyInternal medicineMetastasis

Abstract

fetched live from OpenAlex

Locoregional recurrent/persistent head and neck cancer following primary treatment is a significant challenge as it is usually difficult to treat and has worse outcomes compared to the primary setting. Surgical resection of a local or regional recurrence offers the best chance of cure when feasible. Local recurrence outcomes vary by subsite with laryngeal recurrences having the best prognoses and hypopharynx having the worst. Instances of persistent neck masses following primary nonsurgical treatment can be evaluated with positron emission tomography (PET) with CT (PET-CT) when there is no definitive diagnosis of a recurrence/persistence. Reirradiation with or without chemotherapy can be considered for primary treatment when surgery is not an option, for adjuvant treatment following salvage surgery, or for palliation. Immunotherapy represents a newer class of chemotherapeutic agents. Current guidelines recommend enrollment in clinical trials especially when surgery is not an option as outcomes remain universally poor in the recurrent/persistent setting.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0200.014

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 head0.092
GPT teacher head0.398
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations21
Published2018
Admission routes1
Has abstractyes

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