Tree-Based Prediction of Influential Factors and Information Mining
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
In minimally invasive gastrointestinal surgery (IPI), local sedative and analgesic drugs are required, and a new type of drug, "R-drug", has yet to be studied non-intervention ally. This paper analyzes and explores the vital signs, adverse effects and patient satisfaction of IPI based on the real performance data of new and traditional sedative drugs in clinical trials. In this paper, we first cleaned, coded and normalized the data, then based on multivariate visualization analysis, we found that there were significant differences between different drug groups regarding each adverse reaction, and we conducted chi-square test on different drug groups regarding each adverse reaction, and we found that there were significant differences between different drug groups regarding intra-operative adverse reactions, and only "nausea and vomiting" and "abdomen and vomiting" were found in the post-operative adverse reactions. Among the postoperative adverse reactions, only "nausea and vomiting" and "abdominal distension and abdominal pain" showed significant differences. Regarding the prediction of adverse reactions, this paper up-sampled the dataset and built a model based on the K nearest neighbor algorithm, and the classification AUC of the model on the tested dataset was above 0.92, and the confusion matrix and ROC diagram were made to visualize the specific testing of the model.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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