Analysis on the Inborn Defect Data of Perinatal With Chilopalatognathus in the City of Hengyang from 2002 to 2009
Bibliographic record
Abstract
Objective To understand the incidence rate of inborn chilopalatognathus and its dynamic state changing for formulating a precaution of inborn chilopalatognathus.Methods Collected and analyz the inborn defect data in hospital in the city of Hengyang from 2002 to 2009.Results The average incidence rate of inborn chilopalatognathus was 15.19 in every ten thousand,the incidence rate of male was 17.51 in every ten thousand,the incidence rate of female was 9.57 in every ten thousand,the male-female incidence rate was 1.79 to 1(P=0.000),the difference between man and female was significant;the city incidence rate was 9.05 in every ten thousand,the country incidence rate was 17.89 in every ten thousands,there was a significance between them(P=0.000);the fourth quarter incidence rate was 25.32,9.24,9.63,19.32 in every ten thousand respectively,the fourth quarter incidence was significant(P=147.000,P=0.031);the incidence rate in different age group pregnant woman was 65.81,15.22,12.96,9.51,56.29 in every ten thousand respectively,the difference between them was significant(P=0.000).Conclusion The incidence rate of inborn chilopalatognathus during the 8 years in Hengyang city was rising;the country incidence rate was higher than citys;the incidence rate in winter and spring were higher than summer and autumns;the age of pregnant women lower than 20 and equal or higher than 35 was the high stage in the incidence rate of inborn chilopalatognathus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".