DIAGNOSTIC SLEEP STUDIES IN CHILDREN WITH MEDICAL COMPLEXITY: DO THEY CHANGE MANAGEMENT?
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) can be described by four characteristics: a chronic complex medical condition, severe functional limitations, substantial healthcare needs, and high healthcare utilization. CMC and their families are faced with countless medical tests and challenging treatment decisions. CMC are predisposed to sleep disordered breathing (SDB), as their conditions often affect the central nervous system, neuromuscular tone, and craniofacial structures. A polysomnogram (PSG) is the gold standard to diagnose and determine SDB severity. If SDB is diagnosed, treatment options are often invasive and may not be in the best interests of the child. As a result, outlining treatment options and understanding a family’s wishes prior to proceeding with a PSG is prudent. Sharing knowledge and decision-making with a family may decrease costs to both the child and the healthcare system. OBJECTIVES: Our aim was to: a) identify if SDB diagnosis in CMC impacts clinical care; b) explore whether or not families’ wishes were discussed prior to PSG completion. DESIGN/METHODS: Our Complex Care Database was searched for CMC who underwent a baseline PSG from January 1, 2009 to June 15, 2015. PSGs completed for follow-up or ventilation titration were excluded. Health records were reviewed to determine demographics, medical history, families’ wishes, PSG results, and their impact on clinical care. Descriptive statistics were used to summarize results. RESULTS: 181 patients were identified from the initial search; 96 patients met inclusion criteria. 48 (50%) were male. Mean (SD) age was 4.10 (3.97) years. 32 (33%) had moderate to severe obstructive apnea, 10 (10%) had moderate to severe central apnea, and 3 (3%) had both. Of those diagnosed, 9 had surgery, 23 had respiratory technology initiated and 3 had both surgery and respiratory technology initiated. 4 patients were urgently admitted to hospital. Only 3 out of 96 patients had clear documentation of their families’ wishes in regards to the PSG prior to its completion. CONCLUSION: Approximately one half of CMC referred for PSG had a diagnosis of clinically significant SDB. PSG results led to significant intervention in 78% of those diagnosed. Of those remaining, treatment options were either considered too risky, or did not align with the families’ wishes. Recognizing the burden of medical tests for both the child and the healthcare system, it is prudent to clarify a family’s wishes prior to conducting a PSG. Shared-decision making will help ensure clinical investigations and management remain in the best interests of the child.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".