Chronic Ventilator Need in the Community: A 2005 Pediatric Census of Massachusetts
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to describe the population of children with chronic mechanical ventilation in Massachusetts and their patterns of medical care. PATIENTS AND METHODS: Investigators surveyed all of the Massachusetts home ventilator clinics, pediatric pulmonary services, hospital-based pediatric services for special health care needs, insurers, home care vendors, nursing agencies, the Massachusetts Department of Public Health, selected individual providers, and rehabilitation and long-term care facilities providing services to children with chronic respiratory support needs. Support was defined as daily use of noninvasive, negative-pressure, or invasive/transtracheal ventilators. Subsequent matching of demographic data, including date of birth, zip code, and gender supported maximal census yield without duplications. Geographic information systems were used to create distribution maps and estimate distances between children with chronic mechanical ventilator needs and key resources. RESULTS: A total of 197 children were identified as requiring chronic mechanical respiratory support in Massachusetts in 2005, which was a nearly threefold increase in this population in the 15-year interval since the last census. Congenital or perinatal-acquired neurologic or neuromuscular disorders constituted the majority of primary diagnoses (n = 107 [54%]). Chronic lung disease attributed to prematurity represented only 7% of the sample. CONCLUSIONS: Children receiving chronic mechanical respiratory support are a growing population. The shift in underlying diagnoses from pulmonary disease to neurogenic respiratory insufficiency has implications for hospital and community-based providers from all disciplines in extending services to the home setting. Barriers encountered when performing this study, however, reflect an overall lack of coordination among the many individuals and agencies involved in their care. Coordinated and centralized care efforts require a clear and managed flow of information; census reports such as this one are only the beginning. Direct needs assessments and quality-of-life surveys from families are needed to design and implement programmatic changes and advocacy efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".