SafetyNET: An interdisciplinary research program to support a safety culture for spinal manipulation therapy
Bibliographic record
Abstract
A team of interdisciplinary research leaders have taken a novel approach to support a patient safety culture for spinal manipulation therapy (SMT) providers. The aim was to devise a team-based approach to identify modifiable and non-modifiable patient and provider risk factors. SafetyNET has four main areas of inquiry, led by five principal investigators. The SafetyNET initiative began with qualitative research regarding patient safety, including identification of potential facilitators and barriers to patient safety research. Simultaneously, a health law team is conducting research to identify potential barriers to patient safety research, including the risk of litigation. Feedback from both the qualitative and health law team is informing the development and implementation of an active surveillance reporting and learning system. This information in turn, helps inform our basic science team toward investigation of the potential mechanism of action for SMT-related adverse events. One outcome of the SafetyNET initiative is to provide a model for other disciplines and jurisdictions with respect to improving safety in procedures common to several regulated health disciplines. This article belongs to the Special Issue: Ensuring and Improving Patients Safety in Integrative Health Care.
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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 | 0.117 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".