A comparison of several regression models for analysing cost of CABG surgery
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
Investigators in clinical research are often interested in determining the association between patient characteristics and cost of medical or surgical treatment. However, there is no uniformly agreed upon regression model with which to analyse cost data. The objective of the current study was to compare the performance of linear regression, linear regression with log-transformed cost, generalized linear models with Poisson, negative binomial and gamma distributions, median regression, and proportional hazards models for analysing costs in a cohort of patients undergoing CABG surgery. The study was performed on data comprising 1959 patients who underwent CABG surgery in Calgary, Alberta, between June 1994 and March 1998. Ten of 21 patient characteristics were significantly associated with cost of surgery in all seven models. Eight variables were not significantly associated with cost of surgery in all seven models. Using mean squared prediction error as a loss function, proportional hazards regression and the three generalized linear models were best able to predict cost in independent validation data. Using mean absolute error, linear regression with log-transformed cost, proportional hazards regression, and median regression to predict median cost, were best able to predict cost in independent validation data. Since the models demonstrated good consistency in identifying factors associated with increased cost of CABG surgery, any of the seven models can be used for identifying factors associated with increased cost of surgery. However, the magnitude of, and the interpretation of, the coefficients vary across models. Researchers are encouraged to consider a variety of candidate models, including those better known in the econometrics literature, rather than begin data analysis with one regression model selected a priori. The final choice of regression model should be made after a careful assessment of how best to assess predictive ability and should be tailored to the particular data in question.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it