Maximal Static Respiratory and Sniff Pressures in Healthy Children. A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
RATIONALE: Respiratory muscle strength in children can be assessed by maximal inspiratory pressures (MIP), maximal expiratory pressures (MEP), and sniff nasal inspiratory pressures (SNIP). However, previous studies involved small cohorts of healthy children and reported wide reference ranges. OBJECTIVES: To perform a systematic review to summarize existing reference ranges for MIP, MEP, and SNIP tests in healthy children and to conduct a meta-analysis to develop comprehensive prediction equations. DATA SOURCES: Five databases were searched for relevant studies from database inception to May 29, 2017. DATA EXTRACTION: Study inclusion was limited to publications that evaluated MIP, MEP, and SNIP values in healthy children aged 18 years or younger. Studies were also excluded if testing methodology differed greatly from the 2002 American Thoracic Society Statement on Respiratory Muscle Testing. Requests for raw data were made to authors via e-mail. SYNTHESIS: A total of 18 studies including 3,509 children were systematically reviewed. Diagnostic accuracy of the included studies was assessed using the QUADAS-2 tool, which revealed a high risk of bias for flow and timing and for applicability that may influence the generalizability of our findings. All 18 studies evaluated respiratory pressures in children in seated position. MIP tests were conducted from residual volume, MEP tests from total lung capacity, and SNIP tests from functional residual capacity. The MIP and MEP values in three age groups for boys and girls were summarized using meta-analysis based on individual participant data from five studies containing 1,709 healthy children. Further analyses showed that MIP and MEP were significantly greater in boys than in girls (P < 0.0001). In both sexes, MEP values were always greater than MIP values (P < 0.05). Multivariable random effects models were then performed to establish sex-specific prediction equations. These equations found age, height, and weight to be significant predictor variables. Only two studies with SNIP values from healthy children were included in the review, but they were not part of the meta-analysis. CONCLUSIONS: We summarized the available reference ranges for MIP, MEP, and SNIP tests based on existing literature, especially for three age groups, and developed prediction equations that can be used in pulmonary function laboratories to aid clinicians. Existing literature on SNIP tests is limited, and future studies are encouraged to explore their use in children. Systematic review registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42017072004).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".