Additional file 1 of Percutaneous cannulation is associated with lower rate of severe neurological complication in femoro-femoral ECPR: results from the Extracorporeal Life Support Organization Registry
Bibliographic record
Abstract
Additional file 1: Table S1. ELSO Registry Data Definitions of ECMO complications. Table S2. Sensitivity analyses for the association of percutaneous cannulation with outcomes removing pre-ECMO PH. Table S3. Sensitivity analyses for the association of percutaneous cannulation with outcomes adding pre-ECMO support. Table S4. Sensitivity analyses for the association of percutaneous cannulation with outcomes adding the year of ECPR. Table S5. Sensitivity Analyses for the association of percutaneous cannulation with severe neurological complications adding oxygenation variables. Table S6. Multivariable logistic regression model of percutaneous cannulation and outcomes stratified by center experience of percutaneous cannulation. Table S7. Multivariable logistic regression model of percutaneous cannulation and outcomes stratified by center ECPR volume. Figure S1. Association between percutaneous cannulation and in-hospital mortality across prespecified subgroups. Figure S2. Association between percutaneous cannulation and limb ischemia across prespecified subgroups. Figure S3. Association between percutaneous cannulation and cannulation site bleeding across prespecified subgroups.
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 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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.559 | 0.038 |
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".