Evidence and practice in spine registries
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We performed a systematic review and a survey in order to (1) evaluate the evidence for the impact of spine registries on the quality of spine care, and with that, on patient-related outcomes, and (2) evaluate the methodology used to organize, analyze, and report the "quality of spine care" from spine registries. METHODS: To study the impact, the literature on all spinal disorders was searched. To study methodology, the search was restricted to degenerative spinal disorders. The risk of bias in the studies included was assessed with the Newcastle-Ottawa scale. Additionally, a survey among registry representatives was performed to acquire information about the methodology and practice of existing registries. RESULTS: 4,273 unique references up to May 2014 were identified, and 1,210 were eligible for screening and assessment. No studies on impact were identified, but 34 studies were identified to study the methodology. Half of these studies (17 of the 34) were judged to have a high risk of bias. The survey identified 25 spine registries, representing 14 countries. The organization of these registries, methods used, analytical approaches, and dissemination of results are presented. INTERPRETATION: We found a lack of evidence that registries have had an impact on the quality of spine care, regardless of whether intervention was non-surgical and/or surgical. To improve the quality of evidence published with registry data, we present several recommendations. Application of these recommendations could lead to registries showing trends, monitoring the quality of spine care given, and ultimately improving the value of the care given to patients with degenerative spinal disorders.
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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 | 0.260 | 0.654 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".