Experience with an online prospective database on adolescent idiopathic scoliosis: development and implementation
Bibliographic record
Abstract
Considerable variability exists in the surgical treatment and outcomes of adolescent idiopathic scoliosis (AIS). This is due to the lack of evidence-based treatment guidelines and outcome measures. Although clinical trials have been extolled as the highest form of evidence for evaluating treatment efficacy, the disadvantage of cost, time, lack of feasibility, and ethical considerations indicate a need for a new paradigm for evidence based research in this spinal deformity. High quality clinical databases offer an alternative approach for evidence-based research in medicine. So, we developed and established Scolisoft, an international, multidimensional and relational database designed to be a repository of surgical cases for AIS, and an active vehicle for standardized surgical information in a format that would permit qualitative and quantitative research and analysis. Here, we describe and discuss the utility of Scolisoft as a new paradigm for evidence-based research on AIS. Scolisoft was developed using dot.net platform and SQL server from Microsoft. All data is deidentified to protect patient privacy. Scolisoft can be accessed at (www.scolisoft.org). Collection of high quality data on surgical cases of AIS is a priority and processes continue to improve the database quality. The database currently has 67 registered users from 21 countries. To date, Scolisoft has 200 detailed surgical cases with pre, post, and follow up data. Scolisoft provides a structured process and practical information for surgeons to benchmark their treatment methods against other like treatments. Scolisoft is multifaceted and its use extends to education of health care providers in training, patients, ability to mine important data to stimulate research and quality improvement initiatives of healthcare organizations.
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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 | 0.069 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".