Survey on childhood solid malignant tumors in cases admitted to mofid pediatric hospital from 1996-2010: a single-center study.
Bibliographic record
Abstract
BACKGROUND: As children comprise a considerable proportion of our population, the importance of local epidemiologic research and geographic and racial differences can't be disputed on childhood malignancies. METHODS: In this descriptive retrospective study, we extensively reviewed the medical records of patients younger than 15 years of age, diagnosed with solid malignant tumors, from 1996 to 2010, using the last version of International Classification of Childhood Cancers. RESULTS: In our study the order of incidence of solid malignancies was relatively similar to the other national studies, with lymphomas and Central Nervous System (CNS) tumors as the most common, followed by Sympathetic Nervous System (SNS) tumors, soft tissue sarcomas and renal tumors. The peak age of diagnosis was between 1 and 4 years old. In our study, the overall male to female ratio was 1.38, with a trend towards male dominance in the older age groups. We also observed a disturbing trend of childhood solid malignancies. The total number of cases almost doubled from 2009(54(6.9%)) to 2010(96(12.2%)) .This trend was particularly detected in CNS and SNS tumors. Further analysis showed that malignant CNS tumors had played a more pronounced role in this change. CONCLUSION: Changes in trends of some tumor categories have illustrated a desperate need to further research in regional and national levels. Also the gathered data can be used to make more accurate programs for a better control of cancer and to help policymakers to allocate more evidence-based resource for hospitals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.001 | 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".