CAM and Pediatric Oncology: Where Are All the Best Cases?
Bibliographic record
Abstract
Background. Use of complementary and alternative medicine (CAM) by children with cancer is high; however, pediatric best cases are rare. Objectives. To investigate whether best cases exist in pediatric oncology using a three-phase approach and to compare our methods with other such programs. Methods. In phase I, Children's Oncology Group (COG) oncologists were approached via email and asked to recall patients who were (i) under 18 when diagnosed with cancer, (ii) diagnosed between 1990 and 2006, (iii) had unexpectedly positive clinical outcome, and (iv) reported using CAM during or after cancer treatment. Phase II involved partnering with CAM research networks; patients who were self-identified as best cases were asked to submit reports completed in conjunction with their oncologists. Phase III extended this partnership to 200 CAM associations and training organizations. Results. In phase I, ten cases from three COG sites were submitted, and most involved use of traditional Chinese medicine to improve quality of life. Phases II and III did not yield further cases. Conclusion. Identification of best cases has been suggested as an important step in guiding CAM research. The CARE Best Case Series Program had limited success in identifying pediatric cases despite the three approaches we used.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".