A Feasibility Study Involving Recruitment and Screening for Aphasia in Acute Stroke: Emerging Viability of the English Adaptation of the Language Screening Test (LASTen)
Bibliographic record
Abstract
OBJECTIVES: We describe recruitment feasibility for language screening in acute stroke using the English adaptation the Language Screening Test (LASTen), originally developed in French. We also elucidate preliminary measurement properties of LASTen in patients with and without aphasia. DESIGN: Prospective eligibility tracking, recruitment, and screening for aphasia using the 2 parallel forms, LASTen-A and LASTen-B. SETTING: The Neurovascular Unit and the Transient Ischemic Attack and Minor Stroke Unit of a tertiary care hospital. PARTICIPANTS: Stroke patients (N=12) with hyperacute to subacute stroke. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Numbers of eligible patients and recruitment viability, individual performance indicators for both LASTen versions (15 points each) in 12 patients grouped by aphasia status, and reliability of the 2 parallel forms. RESULTS: There were 25 eligible stroke patients over 1 month. All 12 recruited patients consented to testing. The patients ranged in age from 29 to 85 years, and 5 were women. Three patients had intracerebral hemorrhage, and 6 had aphasia (mild to severe). The median LASTen scores in patients with and without aphasia were 10 (interquartile range, 8) and 15 (interquartile range, 0), respectively. Five patients had discrepant scores across versions involving a 1-point difference. One patient with aphasia had a 5-point difference, demonstrating improvement on the second version. The Pearson correlation coefficient was 0.95 for parallel form reliability. CONCLUSIONS: Our study confirmed that LASTen appears to function as designed. There was score heterogeneity for patients with aphasia and desired ceiling effects for those without aphasia, alongside excellent parallel form reliability. The findings provide the impetus for a large-scale diagnostic accuracy trial in acute stroke patients.
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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 | 0.067 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".