The effectiveness of scoliosis screening programs: methods for systematic review and expert panel recommendations formulation
Bibliographic record
Abstract
BACKGROUND: Literature on scoliosis screening is vast, however because of the observational nature of available data and methodological flaws, data interpretation is often complex, leading to incomplete and sometimes, somewhat misleading conclusions. The need to propose a set of methods for critical appraisal of the literature about scoliosis screening, a comprehensive summary and rating of the available evidence appeared essential. METHODS: To address these gaps, the study aims were: i) To propose a framework for the assessment of published studies on scoliosis screening effectiveness; ii) To suggest specific questions to be answered on screening effectiveness instead of trying to reach a global position for or against the programs; iii) To contextualize the knowledge through expert panel consultation and meaningful recommendations. The general methodological approach proceeds through the following steps: Elaboration of the conceptual framework; Formulation of the review questions; Identification of the criteria for the review; Selection of the studies; Critical assessment of the studies; Results synthesis; Formulation and grading of recommendations in response to the questions. This plan follows at best GRADE Group (Grades of Recommendation, Assessment, Development and Evaluation) requirements for systematic reviews, assessing quality of evidence and grading the strength of recommendations. CONCLUSIONS: In this article, the methods developed in support of this work are presented since they may be of some interest for similar reviews in scoliosis and orthopaedic fields.
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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 | 0.480 | 0.685 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".