Management Outcomes of Maxilary Sinus Maligancies: A Fifteen Year Study at Radiotherapy Department in a Tertiary Health Facility in Ibadan, South-West, Nigeria
Bibliographic record
Abstract
INTRODUCTION: Maxillary sinus malignancies are rare worldwide. The disease usually presents at an advanced stage making its management challenging for all the medical personnel involved in its treatment. Because of its location deep within the maxilla and its proximity to critical surrounding structures, radiotherapy plays an integral role in sterilizing the area of malignant cells. OBJECTIVE: The aim of this study is to assess the management outcomes of maxillary sinus malignancies at the radiotherapy clinic of the University College Hospital, Ibadan.METHODS: A retrospective study of a total of 108 patients with histological diagnosis of maxillary sinus malignancies registered from January 1995 to December 2009 was done. The data was analysed using the Statistical Package for Social science (SPSS) version 21, and statistical significance of association between variables was assessed using Chi-square test at p<0.05. Ethical clearance was obtained from the Health Research Ethics Committee of UCH.RESULTS: A total of 108 patients with histologically confirmed maxillary antrum malignancies were seen over the study period. The mean age of the patients was 50.3±2.8years. The sex distribution showed 65(58.3%) males and 45(41.7%) females. Multimodality management was the primary mode of treatment. Histology and mode of treatment were found to be of prognostic significance. Only 6.4% of the patients had complete remission, while 14.8% and 50% had no remission and partial remission respectively.CONCLUSION: Majority of the patients had partial remission or no remission in our study despite the combination of surgery and radiotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".