Introduction to Focus Issue in Spine Oncology
Bibliographic record
Abstract
In Brief Study Design. Narrative review. Objectives. To outline and explain the organizational evidence-based medicine (EBM) technique used in the articles for this focus issue and discuss the suitability of spine oncology to this technique. Summary of Background Data. EBM is research-derived evidence and patient preferences, applied in the context of clinical experience and expertise. In the past, most clinical recommendations were based solely on the scientific evidence with little or no regard for clinical expertise and patient preference. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) technique is based on a sequential assessment of the quality of evidence, followed by weighing benefits against risks, leading to a subsequent treatment recommendation, either strong or weak. Weak is still an endorsement of treatment but not for all patients. Methods. A literature review was conducted using MEDLINE addressing EBM and grades of recommendations. The GRADE Methodology was then discussed among clinical experts in oncology and methodologists to determine appropriateness for this focus issue. Results. The strength of recommendations based on evidence quality and clinical expertise was performed by an international group of spine oncology experts and methodologists using the GRADE methodology. Specifically, a systematic review followed by a modified Delphi technique was carried out to answer 2 specific questions on a range of topics in primary and secondary spine oncology. The strength of the recommendation is given priority over the quality of the evidence, thus differentiating the judgments regarding the quality of evidence from assessment of the strength of recommendations. This is critical as many questions in oncology lack high quality evidence due to low prevalence of the disease or complex research design issues, but clinical direction is still required. Conclusion. Key opinion leaders using the GRADE System made treatment recommendations based on systematically reviewed evidence, blended with clinical expertise and patient preference on critical, controversial questions in spine oncology. The Grading of Recommendations, Assessment, Development, and Evaluation System provides an objective, transparent process to combine best available evidence with clinical expertise and patient preference, and is ideal to provide treatment recommendations in spine oncology. Each focus issue article uses Grading of Recommendations, Assessment, Development, and Evaluation to provide either a strong or weak recommendation on 2 key questions in spine oncology.
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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 | 0.032 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".