Characterization of Speech and Language Deficits in the Postanesthesia Care Unit: A Novel, Qualitative Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: Assessing recovery after general anesthesia is complicated because patients must be sufficiently arousable to pay attention to the verbal instructions probing cognitive function. All delirium screens that have been used in the postanesthesia care unit (PACU) rely heavily on a patient's ability to process language information and respond appropriately. However, postanesthesia effects on speech-language functions have not been specifically evaluated. Here we present a novel, qualitative PACU evaluation for cognitive domains critical for speech-language functions, and we compare this assessment against a multidomain neurocognitive examination: Telephonic Montreal Cognitive Assessment (t-MoCA). This may be used to identify trajectories of neurocognitive recovery after surgery with general anesthesia and provide neuroanatomic correlates for specific deficits. METHODS: We investigated 48 patients undergoing general anesthesia for noncardiac and nonneurologic elective surgeries. Preoperatively, participants were administered our PACU speech-language assessment (PACU-SLA) and t-MoCA. Both assessments were again administered postoperatively in the PACU. Different versions of PACU-SLA were administered pre- versus postoperatively. Twenty-three participants randomly received the same t-MoCA versions (group AA), and 25 participants received different versions (group AB), pre- versus postoperatively. Assessments were administered ≥30 minutes after PACU arrival, and before PACU discharge. Statistical analysis was performed using Wilcoxon-signed-rank tests for nonnormally distributed paired data, analysis of covariance for assessing the impact of group (AA versus AB) and preoperative scores on postoperative scores, and χ2 tests for unpaired categorical data (P < .05 indicating significance). RESULTS: After adjusting for preoperative scores, the postoperative t-MoCA scores of group AB were significantly lower than group AA (F[1-46] = 21.7, P < .001). Similarly, the t-MoCA scores of episodic-memory (delayed-recall) decreased in group AB (F[1-46] = 48.6, P < .001). For PACU-SLA, there were no postoperative changes in auditory-comprehension and object-naming scores, but a decrease was observed in (1) scores of a 9-point narrative-production task of expressive-fluency (n = 48; median [25th-75th]; preoperative: 9[9-9], postoperative: 7[7-8], P < .001), and (2) total words generated in 2 30-second tasks of verbal-fluency (n = 48; median[25th-75th]: preoperative: 23[12.5-33.5], postoperative: 16.5[9.5-26.5], P < .001). Scores on a 4-point sentence-repetition task were also noted to decrease postoperatively (n = 48; median[25th-75th]; preoperative: 4[3-4], postoperative: 4[3-4], P = .04). When grouping participants by preoperative cognitive status (pMCI, n = 9; preoperative normal, n = 39), both groups showed postoperative changes in verbal-fluency (F[1-46] = 6.97, P = .01) and narrative-production scores (F[1-46] = 5.51, P = .02). CONCLUSIONS: The PACU-SLA revealed lower fluency (hypophonia) with relatively intact comprehension, naming, and repetition, during recovery from general anesthesia. These deficits share features with transcortical motor aphasia.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".