Screening tool for late‐effect pediatric neuro‐oncological clinics: A treatment‐oriented questionnaire
Bibliographic record
Abstract
BACKGROUND: Many survivors of pediatric brain tumors (SPBTs) suffer from long-term late effects (LEs). Our aim was to create a practical screening tool for detecting LEs in this population. Such a screening tool will improve our ability to identify those patients who may benefit from treatment in LE clinics while focusing on individual relevant issues. PROCEDURE: We developed the Treatment-Oriented Screening Questionnaire (TOSQ); a self-reported, risk-based questionnaire that addresses all LEs SPBTs can potentially suffer. As a basis for the TOSQ design we used the Long-Term Follow-Up Guidelines published by the Children's Oncology Group. Output includes individual recommendations for further treatment. We prospectively assessed whether the TOSQ can accurately detect treatment targets in SPBTs by comparing patient and caregiver questionnaire scores with physician evaluations. Data are presented from 41 SPBTs. RESULTS: The TOSQ is a precise screening tool for identifying LEs in SPBTs based on the significant correlation (P < 0.05) that was found between parental scores and physician evaluations. Statistical testing proved that parents are a good source of information about child's health status, and that TOSQ accurately reflects the correlation between patient difficulties and quality of life. CONCLUSIONS: The TOSQ is the first described screening tool for identification of LEs designed specifically for SPBTs. It is simple to use and provides a valid, comprehensive and economic assessment followed by targeted treatment plan for each patient. By repeatedly using the TOSQ over the years, we can improve our ability to detect and give focused treatment to those who require assistance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".