Prevalence and Characteristics of Oral Squamous Cell Carcinoma in Fiji: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Oral squamous cell carcinoma (OSCC) is malignant epithelial neoplasm that affects the oral cavity. Despite the large number of studies on OSCC in the literature, details on the demographic profile of these lesions in different populations are limited, especially amongst the Fijian population, thus this study aimed to determine the prevalence and characteristics of OSCC cases diagnosed from 2011 to 2015 at the Colonial War Memorial Hospital (CWMH), Suva, Fiji Islands. MATERIALS & METHODS: A retrospective quantitative study was carried out at the CWMH Histology department looking at OSCC cases diagnosed from 2011–2015. All confirmed cases were included in the study and excluded cases were suspected cases that were pending diagnosis and children. An extraction sheet was used for data collection which was designed to meet the requirements of study finding. This data was entered in SPSS software and then analyzed using the simple analytical tool as bar graphs and tables. RESULTS: There were 74 OSCC cases diagnosed in the study period. The occurrence of OSCC cases over the 5-year period was fluctuant with the year 2013 recording the highest number of cases (24 cases, 32%). During the reported period, more men suffered (53 cases, 72%) from this condition with the tongue (40%) being the commonest oral cancer site followed by the lip and vermillion border (28%). Individuals in the 40 to 60 years’ age group (35 cases, 47.3%) were mostly affected with the I-taukei population (38 cases, 51.4%) recording the highest occurrence of OSCC. The results show that, the common occurrence is the tongue (40.5%) while the least presentation sight is Buccal mucosa/buccal sulcus area (8.1%). CONCLUSION: The study concluded that more men suffer from oral squamous cell carcinoma compared to woman and the ethnicity that was most affected was I-taukei population. More detailed record keeping and reporting of oral cancer is needed to help evaluate progress in controlling oral cancer in the Fiji Islands. This availability of geographic data would further assist the Ministry of Health of Fiji in data collection and planning of preventative activities
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".