Abstract TP97: Feasibility and Reliability of Patient-Reported Scores to Assess Long-Term Functional Outcomes in Stroke: A Sub-Group Analysis of the MaRISS Trial
Bibliographic record
Abstract
Obtaining delayed functional outcomes such as the modified Ranking score (mRS) after stroke is challenging in clinical practice, often resulting in missing data. Self-reported outcomes are an alternative to functional outcome collection, but their reliability and accuracy are not well-established Methods: In this subgroup analysis of the Mild and Rapidly Improving Stroke Study (MaRISS), we aimed to test the feasibility and reliability of the MaRISS Patient Reported Outcome (PRO) survey tool for obtaining delayed functional outcomes in low (0-5) National Institute of Health Stroke Scale ischemic stroke, particularly the modified Rankin Score (mRS), by assessing its inter-rater reliability with a score calculated by a clinician through a structured telephone interview. Other scores were compared as secondary outcomes. 125 surveys were distributed between January 2017 and July 2018. The tool consisted of an online survey aiming to collect information regarding the patient's functional status 90 days after stroke including the mRS (utilizing the mRS 9-Q version), Stroke Impact Scale-16 (SIS), Barthel Index (BI), European Quality of Life-5D-5L (EQ-Index). All participants also completed these scales administered by trained personnel through a structured telephone interview. Cohen's weighted kappa coefficients (κ) with 95% confidence intervals (CI) were calculated to assess the reliability of the PRO Tool Results: Of 125 surveys sent, 55/125 (46.4%) participants opened and started the survey but only 44/125 (36.8%) completed it entirely (mean age 62±12.5, 54.6% female, white 79.6%, NIHSS 2.3±1.8, ischemic stroke 84%, 16% TIA). 52 subjects completed both the online mRS 9-Q PRO and the telephone version (Table). The weighted kappa for the comparison of PRO mRS and the clinician-performed mRS was moderate [κ 0.53, SE 0.10; 95% CI (0.3-0.7)] as well as for the SIS (κ 0.43, SE 0.09) and EQ-Index (κ 0.40, SE 0.10) scores, whereas for BI was fair (κ 0.32, SE 0.15) Discussion: Engaging participants after hospital discharge remains difficult. Although participant completion rates for self-reported outcomes were low, the MaRISS PRO Tool showed moderate reliability. These findings should be confirmed in larger samples with a focus on improving participant engagement
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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".