A Comparative Analysis to Determine Clinical Factors Influencing Technicity Index Between Hospitals [3J]
Bibliographic record
Abstract
INTRODUCTION: Canadian national guidelines recommend minimally invasive hysterectomy as the preferred route due to less complications and shorter hospital stays. An index known as Technicity quantifies the number of minimally invasive hysterectomies relative to the total performed annually. Our aim was to compare a tertiary care and community hospital to determine factors leading to differences in Technicity Index (TI). METHODS: We reviewed all hysterectomies performed at Royal Alexandra Hospital (RAH—tertiary care), and Grey Nun's Hospital (GNH—community) in Edmonton, Alberta, Canada in 2011. There were 1,053 charts reviewed. We collected data regarding type of procedure, patient BMI, number of medical and surgical comorbidities, length of hospital stay, and complication rate. RESULTS: Compared to abdominal and vaginal hysterectomy (AH, VH), the laparoscopic approach was associated with shorter hospital admission (1.8 days versus 2.5 days for AH and VH). With laparoscopic hysterectomy (LH), blood loss was decreased and patients were less likely to need a blood transfusion (AH [8%], VH [2%], LH [1%]). The TI was significantly increased at the community hospital (GNH 48.2%) compared to the tertiary care hospital (RAH 23.7%) (P<.001). The patients treated at RAH had a greater number of medical (3.0 [RAH] versus 1.9 [GNH]) and surgical (2.2 [RAH] versus 1.2 [GNH]) comorbidities and higher BMI (30.4 versus 29.0). CONCLUSION: The benefits of minimally invasive hysterectomy are supported in this study. Differences in patient demographics such as BMI, medical and surgical comorbidities may influence the percentage of minimally invasive hysterectomies performed at an institution.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".