Bibliographic record
Abstract
Waterston’s seminal paper describing the influence of pulmonary disease, birth weight and associated congenital anomalies on outcome of infants treated for EA,2 provided a historical basis for comparison of subsequent outcome-based classification systems. Advances in medical care have now rendered the Waterston classification outdated. Spitz et al. have described a simplified classification system for the modern era (Table 34.1), based on birth weight and the presence or absence of major congenital heart disease.18 A Montreal classification system places greater emphasis on preoperative ventilator dependence and associated major anomalies as survival determinants.22 Other studies continue to emphasize the negative influence of respiratory distress syndrome (RDS) and pneumonia on outcome,23 and the contribution of aspiration episodes and other respiratory morbidity to late death following repair of EA.24 The influence on outcome of factors directly related to the atresia has received little attention. In this category, ‘long-gap’ EA is associated with significant morbidity,25 and probably increased mortality (although statistical confirmation is difficult because of the relative rarity of long-gap EA).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.032 |
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".