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Record W2530395708 · doi:10.1186/s41199-016-0015-8

Oral and dental health in head and neck cancer survivors

2016· review· en· W2530395708 on OpenAlexaff
Firoozeh Samim, Joel B. Epstein, Zachary S. Zumsteg, Allen S. Ho, Andrei Barasch

Bibliographic record

VenueCancers of the Head & Neck · 2016
Typereview
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMucositisHead and neck cancerCancerIntensive care medicineOral healthPopulationFamily medicineRadiation therapyInternal medicine

Abstract

fetched live from OpenAlex

Therapeutic improvements and epidemiologic changes in head and neck cancer (HNC) over the last three decades have led to increased numbers of survivors, resulting in greater need for continuing management of oral and dental health in this population. Generally, the HNC patient oral health needs are complex, requiring multidisciplinary collaboration among oncologists and dental professionals with special knowledge and training in the field of oral oncology. In this review, we focus on the impact of cancer treatment on oral health, and the oral care protocols recommended prior to, during and after cancer therapy. The management of oral complications such as mucositis, pain, infection, salivary function, taste and dental needs are briefly reviewed. Other complications and their management, including osteonecrosis of the jaw and recurrent/new primary malignancies are also described. This review offers clinical protocols and information for medical providers to assist in understanding oral complications and their management in HNC patients and survivors, and their oral and dental health care needs. Oral and dental care is impacted by the patient's initial oral and dental status, as well as the specific cancer location, type, and its treatment; thus, close communication between the dental professional and the oncology team is required for appropriate therapy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
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 head0.085
GPT teacher head0.438
Teacher spread0.353 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations50
Published2016
Admission routes1
Has abstractyes

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