Oral health in cancer palliative care: cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Patients on anticancer therapy attending palliative care services often have oral health problems, but not enough is known in this regard (in India). This cross-sectional study aimed to elucidate this issue. METHODS: Participants were 98 patients with metastatic cancer (52 males, 46 females) who attended the palliative care clinic and were assessed by a multidisciplinary team consisting of dental health professionals working alongside the palliative care team, between August 2021 and October 2022. Their demographic and medical details were collected from the charts. Details about oral health were assessed using the National Cancer Institute Common Terminology Criteria for Adverse Events for dysphagia and xerostomia, Dental Caries Decayed, Missing, and Filled Teeth (DMFT) index, and the WHO Mucositis scale. Quality of life was assessed using the European Organisation for Research and Treatment of Cancer Quality of Life Module for Oral Health (EORTC QLQ-OH15). RESULTS: Patients had an average age of 58 years, with 46.9% having an Eastern Cooperative Oncology Group score of 1, and 61 (64.9%) receiving anticancer treatment along with palliative care. Dental issues were prevalent, with 39.5% classified as DMFT score grade 2. Subgroup analysis showed higher mean DMFT scores in head/neck cancers, particularly in those receiving radiation therapy (2.3) versus without (1.7), and older age (p<0.05). CONCLUSION: Patients with cancer on anticancer treatment attending a palliative care clinic, especially those with head/neck cancers and older age, had poor oral health. Further prospective research with a dentist on the team is warranted to assess its impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".