Epidemiology, Incidence, and Survival of Rhabdomyosarcoma Subtypes: SEER and ICES Database Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Rhabdomyosarcoma is the most common soft‐tissue sarcoma in children and adolescents and accounts for 3% of all pediatric tumors. Subtypes include alveolar, spindle cell, embryonal, mixed‐type, pleomorphic, and rhabdomyosarcoma with ganglionic differentiation. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database was queried for patients diagnosed with any type of rhabdomyosarcoma between 1973 and 2014. Patient demographics, tumor characteristics, and incidence were studied with χ 2 analysis. Survival was modeled with Kaplan–Meier survival curves and Cox proportional hazards models were used to assess the effect of age and gender on survival. Pleomorphic subtype had higher grade and larger sized tumors compared to other subtypes ( p < 0.05). Pleomorphic and alveolar rhabdomyosarcoma had the worst overall survival with a 26.6% and 28.9% 5‐year survival, respectively. Embryonal rhabdomyosarcoma had the highest 5‐year survival rate (73.9%). Tumor size was negatively correlated with survival months, indicating patients with larger tumors had shorter survival times ( p < 0.05). Presence of higher‐grade tumors and metastatic disease at presentation were negatively correlated with survival months ( p < 0.05). No significant differences in the survival were found between gender or race between all of the subtypes ( p > 0.05). This study highlights key differences in the demographic and survival rates of the different types of rhabdomyosarcoma that can be used for more tailored patient counseling. We also demonstrate that large, population‐level databases provide sufficient data that can be used in the analysis of rare tumors. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2226–2230, 2019
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it