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Record W4289878257 · doi:10.1097/spc.0000000000000608

Oral pain in the cancer patient

2022· review· en· W4289878257 on OpenAlexaff
Firoozeh Samim, Joel B. Epstein, Rachael O. Osagie

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2022
Typereview
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCancer painCancerHead and neck cancerOrofacial painNeurolysisMalignancyHypnosisMEDLINEIntensive care medicinePhysical therapyAlternative medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Oral pain is a common complaint in patients with cancer. This review aims to summarize the knowledge on the causes and approach to management of oral pain garnered over the past 2 years. RECENT FINDINGS: A systematic review and meta-analysis included in the review, assessed cannabinoid versus placebo and showed only a small effect on pain, physical function, and sleep quality. Another review showed that chemical neurolysis as an adjunctive therapy, is effective in patients with pain of shorter chronicity and refractory head and neck cancer-related pain. SUMMARY: Patients with cancer frequently experience oral pain because of a variety of factors. Factors inherent in the type and location of the malignancy, the modality of cancer treatment, and a holistic approach to management together contribute to their overall pain experience. Basic oral care should be implemented wherever possible, before, during, and after cancer treatment.

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)0.001
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.343
GPT teacher head0.527
Teacher spread0.184 · 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
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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

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