Bibliographic record
Abstract
The final recommendations for each article in the Spine Oncology Study Group (SOSG) focus issue are summarized in Table 1. A recommendation has 2 components; first whether it supports or does not support a particular intervention and second the magnitude or strength of this support.Table 1: Summary of Spine Oncology Treatment RecommendationsTable 1: ContinuedTable 1: ContinuedTable 1: ContinuedThe strength of the recommendation for or against an intervention is either strong or weak and is based on the integration of the scientific evidence with consensus expert opinion. The scientific evidence is summarized as high, moderate, low, or very low quality. Consensus expert opinion considers experience, risks, burdens, costs, patient values, and circumstances. A strong recommendation means that most, if not all, clinicians would want to use the intervention, patients would almost all want it, and policy makers should consider it being policy in most situations. A weak recommendation means that in the majority of situations the intervention would be appropriate, but there are a number of situations it might not. From a patient perspective more likely than not patients would choose the intervention, but based on circumstances and values some would not. From a policy perspective extensive debate and diverse input would be required.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.006 |
| Insufficient payload (model declined to judge) | 0.305 | 0.151 |
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".