Development of a mathematical model for predicting postoperative pain among patients with limb injuries
Bibliographic record
Abstract
A mathematical model is devised to predict the probability of development of postoperative pain among patients of young age, operated on in a planned manner for the limb injuries. As the model predictors we selected: the level of pain before operation, determined by the visual analog scale, result of evaluation of cognitive abilities by the Montreal scale and level of the mean blood pressure. The application of the developed model makes it possible to improve quality of providing the patients with anesthesiological assistance. The results obtained might be used in the development of information decision support system for a physician-anaesthesiologist for the objectification and automation of the process for determining the probability of development of postoperative pain syndrome. The introduction of such a system into clinical practice will make it possible to reduce the load on the medical staff and decrease the amount of anaesthetising preparations for patients, whose value of the level of pain before operation, determined by the visual analog scale after the operation, does not exceed 3 points, as well as to conduct more adequate analgesia among patients with a higher value of this indicator.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".