Bibliographic record
Abstract
Rh negative women who deliver an Rh positive baby are at risk of developing anti-Rh antibodies.1 Rh positive babies born of these mothers will develop Rh haemolytic disease. This is a severe condition responsible for death in utero or in the neonatal period or severe jaundice with ensuing brain damage. The natural history of the disease has not been described in recent literature. Walker,1 in 1971, reviewed a series of cases from his community. It was found that 14% of affected pregnancies resulted in stillbirths. Of the survivors, 30% had severe disease almost certainly fatal without treatment, while an additional 30% had moderate disease which would manifest as severe hyperbilirubinaemia that untreated may result in brain damage and/or death. Forty per cent of cases would require no treatment. Therefore, it can be estimated that approximately 50% of children with untreated haemolytic disease of the newborn (HDN) will die of the disease or develop brain damage. Similar observations were made in Manitoba, Canada.2 Over 30 years ago it was established that Rh isoimmunisation could be prevented by passive immunisation with anti-Rh (anti-D) γ globulin.3 4 Thereafter, prevention of Rh disease was instituted using postpartum injections of anti-Rh (anti-D) γ globulin; this has been proven to be highly effective.5 In most developed, high income countries, all Rh negative postpartum women whose babies are Rh positive …
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".