Bibliographic record
Abstract
predicting respiratory outcomes (1, 6).Evaluation of the data from Jensen and colleagues may provide supporting or refuting evidence for this maturational cutoff for defining BPD.We agree that our goal should be to adopt an evidenceinformed or data-driven approach to identify neonates of extremely low gestational age with a future risk of developing pulmonary and neurodevelopmental issues.The critical task for members of the neonatal community is to decide what we aim to achieve in defining BPD.Do we want to identify nearly all children who may develop adverse outcomes (minimum false negatives), do we want to rule out all who may not develop adverse outcomes (minimum false positives), or do we want to settle for a compromise and accept a middle ground?The answer may require careful thinking.We may want to use criteria with minimum false negatives when identifying children for closer surveillance during childhood, to predict and manage respiratory adverse outcomes; use criteria with minimum false positives when testing experimental therapies, to rationalize exposure for many children; and use "compromise" criteria (with acceptable sensitivity and specificity cutoffs) for quality improvement initiatives, benchmarking, and assessing trends.Discussions about these issues need to happen through an international forum and consensus process, as is currently underway via the International Neonatal Consortium (1).Purpose-defined BPD criteria derived from an international consensus process and supported by data are essential for avoiding ongoing confusion and inconsistency.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.052 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".