A new approach for monitoring healthcare performance using generalized additive profiles
Bibliographic record
Abstract
Recent evidence suggests ever-increasing applications of statistical process control (SPC) in health data analysis. However, the diversity in numbers and types of included variables warrant new statistical control charts. This inclusion can be improved by profiles that monitor a describing functional relationship of the process. In this article, we proposed multiple generalized additive models (GAMs) for profile construction. GAMs permit complex fitting models with simultaneous inclusions of parametric and nonparametric terms. Therefore, GAMs can be applied in health data monitoring with a wide range of explanatory variables. We used two statistics to build control charts: (1) a commonly used univariate statistic in nonparametric profiles; (2) a new proposed multivariate statistic which enables the chart to track the role of each included element in the process changes. The statistics are compared according to their performance in monitoring monthly stroke types, including ischaemic and haemorrhagic strokes of patients with acute stroke in the Mashhad Stroke Incidence Study. Features of the proposed profile are discussed and suggestions are made about the utilized statistics in process monitoring. The results show the successful performance of GAMs in profile monitoring.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".