Coconut Oil as a Novel Approach to Managing Radiation-Induced Xerostomia: A Primary Feasibility Study
Bibliographic record
Abstract
Background. Xerostomia is a common complication following radiation therapy for head and neck cancer (HNC), for which there is no single, universally accepted therapy. Coconut oil has been anecdotally suggested to provide relief for this complication. This study sought to examine the feasibility and effectiveness of coconut oil as a therapy for radiation-induced xerostomia. Methods. A feasibility study was performed among 30 patients with xerostomia subsequent to radiation for HNC. Coconut oil samples were provided along with a protocol for use over a 2-week period and the option to continue if they found it beneficial. Patients were also instructed to keep diaries to document their patterns of use. The Xerostomia-related Quality of Life Scale (XeQOLS) was administered at baseline and 3-month follow-up. Descriptive methods were used to summarize patterns of coconut oil use and paired t-tests were used to assess changes in XeQOLS scores over time. Results. The mean total duration of coconut oil use during the study period was 16 days (1–71). The average number of uses per day was 3 (1–5), with an average amount per use of 5 mL (1.2–8.5). Twelve patients (41.4%) continued coconut oil use beyond the advised period. There was no statistically significant difference in XeQOLS scores pre- and post-treatment. There were no adverse events during the study period. Conclusions. The use of coconut oil as a treatment strategy for xerostomia post-HNC radiation is feasible, inexpensive, and safe. This study demonstrates that there may be a group of HNC patients that benefit from its use.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".