Subtotal median sternotomy for heart surgery
Bibliographic record
Abstract
OBJECTIVE: Many approaches for minimally invasive heart surgery are available. Although they have many advantages, inadequate exposure, mammary artery injury and special tool requirements are known problems. Subtotal median sternotomy (SMS) was developed to overcome such limitations. Comparing the SMS with the standard sternotomy (SS) is the purpose of this study. METHODS: SMS was used in 210 patients (group I) requiring coronary artery bypass grafting and or valvular surgery. This was compared with another 210 patients (group II) in which SS was used. The technical difficulties, incisional discomfort, wound infection, patient satisfaction and hospital stay are the comparison criteria. RESULTS: (1) SMS takes an average of 24 min longer, P<0.15. (2) Incisional discomfort graded (I 'least' to III 'greatest'), group I: (27 Grade I, 176 Grade II, seven Grade III). Group II: (21 Grade I, 183 Grade II, six Grade III), P<0.1. (3) Wound infection: two superficial, two deep in group I, four superficial and one deep in group II, P<0.06, (4) 99% satisfaction and 5.4 days mean hospital stay in group I, 63% and 7.1 days in group II, P<0.01 and <0.03, respectively. CONCLUSION: When comparing the SMS technique with the SS: (1) SMS has statistically significant better patient satisfaction; (2) can be very cost effective due to the short hospital stay and the absence of a need for special instruments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.008 | 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".