Are all patients that count included in palliative care studies?
Bibliographic record
Abstract
<h3>Objective</h3> To compare mortality and rates of significant neurosensory impairment (sNSI) at 18–36 months’ corrected age in infants born extremely preterm across three international cohorts. <h3>Design</h3> Retrospective analysis of prospectively collected neonatal and follow-up data. <h3>Setting</h3> Three population-based observational cohort studies: the Australian and New Zealand Neonatal Network (ANZNN), the Canadian Neonatal and Follow-up Networks (CNN/CNFUN) and the French cohort Etude (Epidémiologique sur les Petits Ages Gestationnels: EPIPAGE-2). <h3>Patients</h3> Extremely preterm neonates of <28 weeks’ gestation in year 2011. <h3>Main outcome measures</h3> Primary outcome was composite of mortality or sNSI defined by cerebral palsy with no independent walking, disabling hearing loss and bilateral blindness. <h3>Results</h3> Overall, 3055 infants (ANZNN n=960, CNN/CNFUN n=1019, EPIPAGE-2 n=1076) were included in the study. Primary composite outcome rates were 21.3%, 20.6% and 28.4%; mortality rates were 18.7%, 17.4% and 26.3%; and rates of sNSI among survivors were 4.3%, 5.3% and 3.3% for ANZNN, CNN/CNFUN and EPIPAGE-2, respectively. Adjusted for gestational age and multiple births, EPIPAGE-2 had higher odds of composite outcome compared with ANZNN (OR 1.71, 95% CI 1.38 to 2.13) and CNN/CNFUN (OR 1.72, 95% CI 1.39 to 2.12). EPIPAGE-2 did have a trend of lower odds of sNDI but far short of compensating for the significant increase in mortality odds. These differences may be related to variations in perinatal approach and practices (and not to differences in infants’ baseline characteristics). <h3>Conclusions</h3> Composite outcome of mortality or sNSI for extremely preterm infants differed across high-income countries with similar baseline characteristics and access to healthcare.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".