Predictive factors at birth of the severity of gastroschisis
Bibliographic record
Abstract
AIM: To establish children born with gastroschisis (GS). METHODS: We performed a retrospective study covering the period from January 2000 to December 2007. The following variables were analyzed for each child: Weight, sex, apgar, perforations, atresia, volvulus, bowel lenght, subjective description of perivisceritis, duration of parenteral nutrition, first nasogastric milk feeding, total milk feeding, necrotizing enterocolitis, average period of hospitalization and mortality. For statistical analysis, descriptive data are reported as mean ± standard deviation and median (range). The non parametric test of Mann-Whitney was used. The threshold for statistical significance was P < 0.05 (Two-Tailed). RESULTS: Sixty-eight cases of GS were studied. We found nine cases of perforations, eight of volvulus, 12 of atresia and 49 children with subjective description of perivisceritis (72%). The mortality rate was 12% (eight deaths). Average duration of total parenteral nutrition was 56.7 d (8-950; median: 22), with five cases of necrotizing enterocolitis. Average length of hospitalization for 60 of our patients was 54.7 d (2-370; median: 25.5). The presence of intestinal atresia was the only factor correlated with prolonged parenteral nutrition, delayed total oral milk feeding and longer hospitalization. CONCLUSION: In our study, intestinal atresia was our predictive factor of the severity of GS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".