Treatment Costs for Nasopharyngeal Cancer by Stages: Patients’ Experience in Sarawak General Hospital
Bibliographic record
Abstract
The study investigates an average direct and indirect costs incurred by Nasopharyngeal Cancer (NPC) patients who received diagnosis, treatment and follow up in the Sarawak General Hospital, Kuching, Malaysia. A total of 299 NPC patients were randomly selected using a primary data collection approach from the Sarawak General Hospital between November 2018-March 2019. Information related to the average direct and indirect costs incurred by NPC patients at various stages and the sources of their financial assistance throughout the treatment periods were assorted. The study reveals that the total average cost of 169 or 56.52% of the NPC patients who received various treatment services in the public hospital is RM13,165 against RM78,860 on 130 or 43.48% of the patients received the same services in both public and private healthcare. Major sources of funding come from patients’ savings, family members, medical insurances, non-profit organization or charity, company healthcare benefits, employees’ provident fund (EPF) as well as subsidy from the government: This study suggests that the treatment cost for cancer patient is high and hence, there is a need to establish a mechanism that can provide a free screening test for NPC as a forward step to cancer prevention, while for policy makers to develop a more supportive initiative to address the needs of the poor patients.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".