Acute and Persistent Postoperative Functional Decline in Children with Severe Neurological Impairment: A Qualitative, Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Children with severe neurologic impairment (SNI) regularly require major surgery to manage their underlying conditions. Anecdotal evidence suggests that children with SNI experience unexpected and persistent postoperative functional changes long after the postoperative recovery period; however, evidence from the perspective of caregivers is limited. The purpose of the study was to explore the functional postoperative recovery process for children with SNI. METHODS: Eligible participants were English-speaking caregivers of children with SNI between 6 months and 17 years who were nonverbal, Gross Motor Function Classification Scale level IV/V, and who had surgery/procedure requiring general anesthetic at a tertiary children's hospital between 2012 and 2022. Demographic and basic health information were collected via surveys and corroborated by a review of the child's electronic health record. Semi-structured interviews were conducted and a thematic content analysis was used to formulate results. RESULTS: Data from 12 primary caregiver interviews revealed four main themes: (1) functional changes and complications in the child; (2) feeling unprepared; (3) perioperative support; and (4) changes to caregiver roles. CONCLUSIONS: Postoperative functional decline in children with SNI was prevalent in our sample. Providing pre-operative information to families to describe this phenomenon should be a regular part of family-informed care.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".