50 Pain severity as a predictor of verbal fluency functioning after COVID-19 illness
Bibliographic record
Abstract
Objective: Published results focusing on language assessment in acutely recovered COVID-19 patients have shown communication problems in this group, including significant cognitive-linguistic disruptions in verbal fluency (Cummings, 2022). Extant research also indicates that poorer health-related outcomes, such as reduced physical functioning and quality of life, co-occur with cognitive difficulties post-COVID-19 infection (Mendez et al., 2021; Tabacof et al., 2022). Understanding what factors may worsen the impact of COVID-19 on cognition, and aspects of language function specifically, is necessary to determine who is at greatest risk of adverse outcomes following infection. Our goal was to examine the effect of health-related outcomes on language abilities, specifically verbal fluency, post-COVID-19 infection. Participants and Methods: 37 adults 19 years and older (M age = 38.78, 67.5% female, 92.5%> high school education) were recruited from British Columbia and Ontario, Canada. Participants provided documentation indicating they had had a COVID-19 infection at least 3 months prior to participation. Participants completed a series of online questionnaires, including the Short Form Health Survey (SF-20), to measure aspects of health-related quality of life. The SF-20 measures dimensions of functioning (physical, social, role) and well-being (mental health, health perception, pain). For each parameter except pain, higher scores indicate better functioning/well-being; for pain higher scores indicate greater pain levels. Participants also completed neuropsychological tests, including measures of verbal fluency, via teleconference. Animals and F-A-S total scores were combined to represent verbal fluency (semantic and phonemic, respectively) performance. To assess the impact of health outcomes on verbal fluency performance, hierarchical regression analyses were conducted. The six SF-20 subscale scores were entered as predictors and verbal fluency score (sum) as the outcome. Age and sex (Male/Female) were controlled for in the model. Results: Age and sex were not significantly related to verbal fluency scores in our sample. After controlling for these demographics, the overall model including SF-20 subscales did not significantly predict fluency performance (F (8, 28) = 1.04, p = .433). However, Pain scores did individually predict verbal fluency performance (B = 5.60, t = 2.53, p = <.05). Unexpectedly, pain ratings were positively associated with fluency scores, such that each increase in pain rating (e.g., “none” to “mild”) was associated with a fluency score increase of 5.60 points (i.e., 5.6 more words stated across administered tasks). Conclusions: These preliminary findings suggest that participants’ self-reported pain severity was positively associated with verbal fluency task performance in our sample (i.e., greater pain severity predicting better fluency). These findings are contrary to substantial evidence showing the deleterious effects of pain on cognitive functions in other populations (Khera & Rangasamy, 2021). It is possible that findings may be explained by a potential unknown intervening variable not included in our model. This is the first study to our knowledge to examine associations between experienced pain and verbal fluency performance post-COVID-19 infection. It will be important for future work to not only utilize more robust measures of pain experiences and explore more areas of cognition and language, but also to employ larger samples and examine a broader set of covariates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".