An exploration of aphasia symptom profiles in speakers of Kalaallisut (West Greenlandic)
Bibliographic record
Abstract
Conceptions and delineations of aphasia syndromes should be informed by crosslinguistic descriptions. This is particularly important for languages that are highly dissimilar to widely studied languages like English. Kalaallisut (West Greenlandic) is one such language – a polysynthetic language of the Inuit-Yupik-Unangan language family spoken by approximately 60,000 people in Greenland and Denmark. A previous study of five Kalaallisut speakers with aphasia (Nedergaard et al., 2020) indicated that non-fluent aphasia in Kalaallisut exhibited different features from those found in English and similar Indo-European languages. However, the previous study was limited by its low number of participants, its focus only on semispontaneous speech narratives, and the fact that a more complete description of the symptom profile of aphasia in Kalaallisut was not available. In the present study, we tested a total of 42 speakers of Kalaallisut on repetition tests, language production tests, language comprehension tests, and working memory tests. We used a combination of qualitative judgments by a speech and language pathologist and hierarchical cluster analysis to analyze the results and were able to distinguish between presence or absence of aphasia, between levels of severity of aphasia, between apraxia of speech/dysarthria and aphasia, and to some extent between different subtypes of aphasia. The present study corroborates the results from Nedergaard et al. (2020) and highlights challenges associated with conducting research in communities with healthcare systems without trained speech and language pathologists, no previous agreed-upon diagnostic criteria, and little access to advanced neuroimaging.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".