Bibliographic record
Abstract
PHYSICIANS AND PATIENTS HAVE BEEN FRUSTRATED by the lack of reliable information on unconventional therapies. To help fill this gap in the area of breast cancer therapy, the Canadian Breast Cancer Research Initiative formed a task force to advise it on how best to promote research into unconventional therapies. As part of the work of the task force, a review of the available literature was carried out for each of the fol-lowing products: Essiac, green tea, Iscador, hydrazine sulfate, vitamins A, C and E, and 714-X. The first article in this series on unconventional therapies for cancer de-scribes the methodology used to obtain and evaluate the information and provides a summary of the findings on Essiac. Subsequent articles will cover the other products. For most of the products reviewed, there has been some indication of possible bene-fit but no definitive evidence. Innovative and collaborative research needed to meet the information needs of growing numbers of patients and their physicians is now being sponsored by the Canadian Breast Cancer Research Initiative. Open commu-nication between patients and physicians is also necessary for the maintenance of an appropriate therapeutic partnership and for the identification and control of side effects. The Ontario Division of the Canadian Cancer Society, a partner in the Can-adian Breast Cancer Research Initiative, supported the preparation of a patient-information piece on unconventional therapies to accompany the series. This item will assist patients who are considering such therapies and will promote open com-munication between patients and their physicians. Résumé LE MANQUE D’INFORMATION FIABLE sur les thérapies non conventionnelles est cause de frustration pour les médecins et les patients. Afin d’aider à combler cette lacune dans le domaine du traitement du cancer du sein, l’Initiative canadienne de recherche sur le cancer du sein a créé un groupe de travail chargé de la conseiller sur les meilleures façons de favoriser la recherche sur les thérapies non conven-tionnelles. Le groupe de travail a notamment étudié la littérature disponible sur les
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.485 | 0.345 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".