Investigation of Long- and Short-Term Relationships Between Cesarean Delivery and Its Effective Factors in Malayer
Bibliographic record
Abstract
INTRODUCTION: Recently, there has been significant increase in the number of operated cesarean compared to the overall number of birth giving. There are several factors affecting the operated cesarean in Iran compared to the birth giving which are to be reviewed in this study. PROCEDURE: The data of the study has been obtained from the registered information in Assistance Section of Health at Hamedan Faculty of Medicine which includes the seasonal data having to do with giving birth of Malayer since the beginning of Winter 2006 to the end of Fall 2013. The assimilation techniques, namely ARDL method and Error Correction Method (ECM) are the main methods to be used in this study. RESULTS: The short-term and long-term coefficients of abnormal view, incongruent status of fetus and pelvis, lack of progression, and the lengthy status are considered significant statistically. The ecm coefficient is -1.3456 in short-term. Also, his coefficient is significant which shows the short-term balance trend to the long-term one. CONCLUSION: The most indispensable affective factor on demanding to run the cesarean operation in short-term and long-term in Malayer are the lengthy-status, lack of progression, abnormal view, and incongruent status of fetus and pelvis, respectively.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".