Additional file 1 of National surveillance of stroke quality of care and outcomes by applying post-stratification survey weights on the Get With The Guidelines-Stroke patient registry
Bibliographic record
Abstract
Additional file 1: eTable 1: ICD-9-CM diagnostic codes used to identify primary acute ischemic stroke hospitalizations in the National Inpatient Sample. eTable 2: Number of hospitals participating in Get With The Guidelines-Stroke per year of analysis. eTable 3: Population totals and proportions by U.S. Census Division for the raw Get With the Guideline-Stroke registry patients included in the final analysis. eTable 4: National characteristics from Table 1 with point estimates with 95% confidence intervals. eTable 5: Characteristics of ischemic stroke patients by year using raked post-stratification weighting by year to the U.S. Population. eTable 6: Characteristics of ischemic stroke patients by year using the Bayesian (flat prior) post-stratification weighting model by year to the U.S. Population. eTable 7: National Characteristics of ischemic stroke stratified by U.S. Division using Bayesian post-stratification weights for 2014. eFigure 1: Distribution of raking post-stratification weights stratified by year. eFigure 2: Distribution of Bayesian post-stratification weights stratified by year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".