Evaluation of the Pediatric Neuro-Oncology Resources Available in Chile
Bibliographic record
Abstract
PURPOSE: Pediatric neuro-oncology resources are mostly unknown in Chile. We report the human and material resources available in Chilean hospitals providing pediatric neuro-oncology services. METHODS: A cross-sectional survey was distributed to 17 hospitals providing pediatric neuro-oncology services (Programa Infantil Nacional de Drogas Antineoplásicas [PINDA] hospitals, 11; private, 6). RESULTS: Response rate was 71% (PINDA, 8; private, 4). Pediatric neuro-oncology services were mainly provided within general hospitals (67%). Registries for pediatric CNS tumors and chemotherapy-related toxicities were available in 100% and 67% of hospitals, respectively. CNS tumors were treated by pediatric oncologists in 92% of hospitals; none were formally trained in neuro-oncology. The most used treatment protocols were the national PINDA protocols. All WHO essential medicines for childhood cancer were available in more than 80% of the hospitals except for gemcitabine, oxaliplatin, paclitaxel, and procarbazine. The median number of pediatric neurosurgeons per hospital was two (range, 2-6). General neuroradiologists were available in 83% of the centers. Pathology specimens were sent to neuropathologists (58%), adult pathologists (25%), and pediatric pathologists (17%). Intensity-modulated radiotherapy, conformal radiotherapy, and cobalt radiotherapy were used by 67%, 58%, and 42% of hospitals, respectively. Only one private hospital performed autologous hematopoietic cell transplant for children with CNS tumors. CONCLUSION: A wide range of up-to-date treatment modalities are available for children with CNS tumors. Our survey highlights future directions to improve the pediatric neuro-oncology services available in Chile such as the expansion of multidisciplinary clinics, palliative care services, long-term cancer survivorship programs, dedicated clinical research support teams, establishing standardized mechanism for sending pathologic specimen for second opinion to international specialized centers, and establishing specialized neuro-oncology training program.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".