B.2 Association between age, frailty, and thrombectomy for ischemic stroke
Bibliographic record
Abstract
Background: Association between age, frailty, and the receipt of thrombectomy for acute ischemic stroke is not well understood. Methods: We conducted a population-based retrospective cohort study of adults hospitalized with an ischemic stroke between 2018 and 2022 in Ontario, Canada. In sex-stratified models, we studied whether frailty (based on hospital-based frailty index: mild, moderate and severe) modified the association between age and thrombectomy by using interaction terms in multivariable modified Poisson regression models. Results: Among 59,346 patients (median age 75 years, 47.0% female) with ischemic stroke 4,454 (7.5%) received thrombectomy, with no sex differences in this treatment. In both sexes, increasing age was associated with decreased use of thrombectomy (adjusted risk ratio [aRR] for every 5-year increase, female = 0.91; 0.89-0.92; male = 0.92; 0.90-0.94). Frailty was not associated with thrombectomy in females (aRR high vs. low frailty = 0.86; 0.68-1.10) or males (aRR high vs. low frailty = 1.10; 0.87-1.39). Furthermore, the interaction between age and frailty was not significant for either sex. Conclusions: Frailty was not associated with thrombectomy in either sex, and it did not modify the association between age and thrombectomy, suggesting a greater role of chronological age compared to frailty in thrombectomy decisions in ischemic stroke patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".