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Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: To propose a risk-adjustment model from insurance claims data, and analyze the changes in cesarean section rates of healthcare organizations after adjusting for risk distribution. \nMethods: The study sample included delivery claims data from January to September, 2003. A risk-adjustment model was built using the 1st quarter data, and the 2nd and 3rd quarter data were used for a validation test. Patients' risk factors were adjusted using a logistic regression analysis. The c-statistic and Hosmer-Lemeshow test were used to evaluate the performance of the risk-adjustment model. Crude, predicted and risk-adjusted rates were calculated, and compared to analyze the effects of the adjustment. \nResults: Nine risk factors (malpresentation, eclampsia, malignancy, multiple pregnancies, problems in the placenta, previous Cesarean section, older mothers, bleeding and diabetes) were included in the final risk-adjustment model, and were found to have statistically significant effects on the mode of delivery. The c-statistic (0.78) and Hosmer-Lemeshow test (x2=0.60, p=0.439) indicated a good model performance. After applying the 2nd and 3rd quarter data to the model, there were no differences in the c-statistic and Hosmer-Lemeshow x2. Also, risk factor adjustment led to changes in the ranking of hospital Cesarean section rates, especially in tertiary and general hospitals. \nConclusion: This study showed a model performance, using medical record abstracted data, was comparable to the results of previous studies. Insurance claims data can be used for identifying areas where risk factors should be adjusted. The changes in the ranking of hospital Cesarean section rates implied that crude rates can mislead people and therefore, the risk should be adjusted before the rates are released to the public. The proposed risk-adjustment model can be applied for the fair comparisons of the rates between hospitals.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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 it