Prevalence of Head and Neck Tumors in Children under 12 Years of Age Referred to the Pathology Department of Children’s Hospital in Tabriz during a 10-year Period
Bibliographic record
Abstract
Background and aims. Head and neck tumors are the most common complaints of people referring to different medical sections, especially in children. The aim of this study was to evaluate the prevalence of these tumors in children less than 12 years of age to provide a better perspective for future studies. Materials and methods. All the files in Department of Pathology at Tabriz Pediatric Hospital from 2001 to 2011 were screened for head and neck tumors in children under 12 years of age. Data including age and gender as well as the type, the location, and benign/malignant characteristic of the tumor were recorded. Data were analyzed by SPSS 15 statistical software, using descriptive statistics and chi-square test. Results. A total of 160 cases were identified. Most of the tumors were benign (68%) and most of the tumors occurred in the neck region (41%). The most frequent benign and malignant tumors were lymphangioma and non-Hodgkin lymphoma, respectively. The majority of benign tumors were found in children younger than 2 years old (P=0.007), but there was no age predilection for malignant tumors. Conclusion. According to our results, benign tumors were more prevalent than malignant ones. Although a low rate of benign tumors in males shows that more attention should be paid to the early diagnosis of head and neck tumors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".