Capturing Intravenous Thrombolysis for Acute Stroke at the <i>ICD‐9</i> to <i>ICD‐10</i> Transition: Case Volume Discontinuity in the United States National Inpatient Sample
Bibliographic record
Abstract
Background Transition from International Classification of Diseases ( ICD ) Ninth and Tenth Revisions ( ICD‐9 and ICD‐10 ) for hospital discharge data was mandated for US hospitals on October 1, 2015. We examined the volume of patients receiving thrombolysis in ischemic stroke (IS) identified using ICD codes within this transition period in the 2015 to 2016 National Inpatient Sample, a weighted 20% sample of all inpatient US hospital discharges. Methods and Results During the ICD‐10 period, 2 case identification strategies were used. Codes for IS were combined with: (1) only the ICD‐10 code for thrombolytic given into a peripheral vein and (2) all new ICD‐10 codes mapped to the ICD‐9 code for all thrombolysis. On visual inspection there was an obvious discontinuity in the volume of patients with IS treated with IV thrombolysis corresponding to 3 time periods: ICD‐9 (study period 1), transition (period 2), and ICD‐10 (period 3). With Strategy 1, analysis using a linear spline with 2 knots shows that the volume of patients with IS treated with IV thrombolysis was significantly different between study periods 1 and 2 (slope difference −1880, 95% CI −2834 to −928, P =0.005), and periods 2 to 3 (slope difference 1980, 95% CI 1207–2754, P = 0.002). With Strategy 2, volumes did not change significantly between periods 1 to 2, though there was a significant difference between periods 2 and 3 (slope difference 719, 95% CI 91–1347, P =0.034). Conclusions The significant discontinuity in thrombolysis volumes for IS during the transition period for ICD‐9 to ICD‐10 coding suggests that more rigorous validation of US administrative data during this time period may be necessary for research, resource planning, and quality assurance.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".