Antireflux Procedures in Children With Neurologic Impairment: A National Survey of Physician Perspectives
Bibliographic record
Abstract
OBJECTIVE: Decision-making about antireflux procedures (ARPs) to treat gastroesophageal reflux disease in children with neurologic impairment and gastrostomy tubes is challenging and likely influenced by physicians' experience and perspectives. This study will explore physician attitudes about ARPs and determine if there are relationships to clinical practice and personal characteristics. METHODS: This is a national observational cross-sectional study that used an electronic questionnaire addressing reported practice, attitudes regarding the ARPs, and responses to clinical vignettes. Participants were physicians in Canadian tertiary-care pediatric settings. Descriptive statistics were used to analyze physician attitudes. Multivariable logistic regression modeling was used to determine associations between physician and practice characteristics and likelihood to consider ARP. RESULTS: Eighty three respondents represented 12 institutions, with a majority from general or complex care pediatrics. There was a wide disparity between likelihood to consider ARP in each clinical scenario. Likelihood to consider ARP ranged from to 19% to 78% depending on the scenario. Two scenarios were equally split in whether the respondent would offer an ARP. None of the demographic characteristics were significantly associated with likelihood to consider ARP. Often, gastrojejunostomy tubes alone were considered (56% to 68%). CONCLUSIONS: There is considerable variability in physician attitudes toward and recommendations regarding ARPs to treat gastroesophageal reflux disease. We did not find a significant association with clinical experience or location of practice. More research is needed to define indications and outcomes for ARPs. This is a scenario where shared decision-making, bringing together physician and family knowledge and expertise, is likely the best course of action.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".