Predictors of extubation readiness in preterm infants: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: A variety of extubation readiness tests have already been incorporated into clinical practice in preterm infants. OBJECTIVE: To identify predictor tests of successful extubation and determine their accuracy compared with clinical judgement alone. METHODS: MEDLINE, Embase, PubMed, Cochrane Library and Web of Science were searched between 1984 and June 2016. Studies evaluating predictors of extubation success during a period free of mechanical inflations in infants less than 37 weeks' gestation were included. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. After identifying and describing all predictor tests, pooled sensitivity and specificity estimates for the different test categories were generated using a bivariate random-effects model. RESULTS: O positive end-expiratory pressure and measured several clinical and/or physiological parameters. Thirty-one predictor tests were identified, showing good sensitivities but low and variable specificities. Given the high variation in test definitions across studies, pooling could only be performed on a subset. The commonly performed spontaneous breathing trials had pooled sensitivity of 95% (95% CI 87% to 99%) and specificity of 62% (95% CI 38% to 82%), while composite tests offered the best performance characteristics. CONCLUSIONS: There is a lack of strong evidence to support the use of extubation readiness tests in preterm infants. Although spontaneous breathing trials are attractive assessment tools, higher quality studies are needed for determining the optimal strategies for improving their accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".