Early palliative care for patients with oral cancer in Sri Lanka: A quasi-experimental study
Bibliographic record
Abstract
ABSTRACT Objectives Oral cancer is prevalent among Sri Lankan men and compromises their quality of life (QOL). Limited research exists on providing early palliative care in low- to middle-income countries. We assessed the effectiveness of an early palliative care intervention in improving psychological distress and QOL among patients with oral cancer in Sri Lanka. Design A quasi-experimental study Setting The study took place at three tertiary care units providing oral cancer treatment in Sri Lanka: the oral and maxillofacial wards at the National Dental Hospital, Colombo (Teaching); the oral and maxillofacial wards at Colombo South (Teaching) Hospital, Karapitiya; and the onco-surgery wards at the National Cancer Institute Maharagama (Apeksha Hospital). Participants The study participants were patients with oral cancer whose definitive diagnosis had been communicated to the patient and had psychological distress (score of ≥4 after screening with the Sinhala version of the Distress Thermometer); awaiting surgery as the first treatment modality; and married with children, including at least one family caregiver with the ability to communicate and read well in Sinhalese. Exclusion criteria were, recurrent oral cancer; a formal psychiatric diagnosis; and receiving or having received any early palliative care intervention. They were divided to 55 controls and 55 cases based on the availability of an accessible Public Health Nursing Officer. Interventions The early palliative care package, comprising six components, was delivered in three sessions by the principal investigator (PI) and Public Health Nursing Officers. Outcome measures The effectiveness was assessed by the level of psychological distress using the Sinhala version of the Distress Thermometer and Level of quality of life using EORTC QLQ 30 with module H&N 35 at baseline (T0), post-intervention (T1), one month (T2), and three months (T3). Results In the intervention group, Distress Thermometer scores significantly decreased from baseline (T0) to three months post-intervention (T3) (6.9 vs. 3.5, p=0.0001). The QOL dimensions improved at all time points for both groups, with the intervention group showing a significantly higher improvement (p=0.0001). Conclusion The novel early palliative care intervention improved the psychological distress and quality of life of patients with oral cancer.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".