Patient positioning outcomes for women undergoing gynaecological surgeries.
Bibliographic record
Abstract
The purpose of this study was to determine if women undergoing gynecological surgery experienced post-operative pain, separate from surgical site pain, in the lower extremities. The study used a quasi-experimental, comparative, exploratory design. A convenience sample of 153 women, mostly day surgery patients, comprised the study sample. The women were divided into two groups; Group I consisted of women for specific procedures requiring the lithotomy position and Group II consisted of women for specific procedures requiring the supine position. Descriptive statistics were used in the data analysis of the positioning guidelines and pain scale scores. Pearson's Moment Correlations were used to determine relationships among the variables and T-test analysis was used to compare the differences between the two groups. The study found that 41% (n = 63) of the women (N = 153) had pain in the lower extremities, separate from incisional site pain, post surgery. Women who were in position for more than 60 minutes had significantly more pain than those who were in position for less than 60 minutes. Of those who reported pain (41%) there was no difference between the supine and lithotomy groups as to the incidence and severity of the pain. Of those in the lithotomy group who reported pain, there was no difference in the reporting of pain based on the type of stirrup used.
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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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".