An evidence-based medicine model for rare and often neglected neoplastic conditions
Bibliographic record
Abstract
OBJECT: The National Institutes of Health recommends strategies to obtain evidence for the treatment of rare conditions such as primary tumors of the spine (PTSs). These tumors have a low incidence and are pathologically heterogeneous, and treatment approaches are diverse. Appropriate evidence-based care is imperative. Failure to follow validated oncological principles may lead to unnecessary mortality and profound morbidity. This paper outlines a scientific model that provides significant evidence guiding the treatment of PTSs. METHODS: A four-stage approach was used: 1) planning: data from large-volume centers were reviewed to provide insight; 2) recruitment: centers were enrolled and provided the necessary infrastructure; 3) retrospective stage: existing medical records were reviewed and completed with survival data; and 4) prospective stage: prospective data collection has been implemented. The AOSpine Knowledge Forum Tumor designed six modules: demographic, clinical, diagnostic, therapeutic, local recurrence, survival, and perioperative morbidity data fields and provided funding. RESULTS: It took 18 months to implement Stages 1-3, while Stage 4 is ongoing. A total of 1495 tumor cases were captured and diagnosed as one of 18 PTS histotypes. In addition, a PTS biobank network has been created to link clinical data with tumor pathology and molecular analysis. CONCLUSIONS: This scientific model has not only aggregated a large amount of PTS data, but has also established an international collaborative network of spine oncology centers. Access to large volumes of data will generate further research to guide and enhance PTS clinical management. This model could be applied to other rare neoplastic conditions. Clinical trial registration no.: NCT01643174 (ClinicalTrials.gov).
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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 | 0.118 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".