Reflections on the introduction of surfactant therapy for neonates with respiratory distress
Bibliographic record
Abstract
When pulmonary surfactant was first detected in the 1950s by Pattle and Clements, many thousands of infants perished each year due to a respiratory illness termed hyaline membrane disease. Hyaline membranes are formed by plasma leaking through damaged endothelial barriers into the terminal bronchiolar: alveolar spaces. Since the leaking plasma lacks erythrocytes, these clots are opaque. Insightful research by Avery and Mead soon led to the suggestion that the neonatal respiratory distress syndrome (RDS) did not arise because of the presence of hyaline membranes, but rather was related to the lack of sufficient pulmonary surfactant, mainly as a result of immaturity. Unfortunately, initial attempts at treating RDS with aerosolized dipalmitoyl-phosphatidylcholine, the major single molecular component, proved unsuccessful. Almost 20 years later, it was demonstrated by Enhorning and Robertson that treating prematurely delivered rabbit pups with natural surfactant prevents respiratory failure. Initially, it appeared unlikely that animal surfactants could be used for therapy with human infants. However, in 1980, Fujiwara demonstrated that a modified bovine surfactant extract promoted gaseous exchange with infants suffering from RDS. Soon a number of bovine and porcine-modified surfactants and two wholly synthetic formulations were shown to alleviate RDS. The present review relates some of the key scientific findings and significant clinical contributions responsible for reducing the neonatal morbidity and mortality associated with RDS. It further describes some of the more recent findings on the biological, biophysical, and physiological significance of pulmonary surfactant in health and disease.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".