An analysis of quantitative measurements of drainage exudate using negative suction in 96 microtia ear reconstructions
Bibliographic record
Abstract
Negative suction drainage is commonly used for the prevention of seromas or hematomas in auricular reconstruction surgery; however, there are few reports regarding the quantitative measurement of negative suction and its relation to disposed time, patient age or microtia type. In the present study, the authors recorded the volume of suction exudate in microtia reconstruction and elaborate on the relevant details of controlling negative suction. A negative suction drainage system was applied in 96 microtia patients between 2007 and 2010. Two small polyethylene drains were inserted adjacent to the concha and the scapha, respectively. The volume of exudate was recorded for three days after surgery and was analyzed according to disposed time, patient age and microtia type. The drains were removed on the third postoperative day, when only a small amount of exudate remained. A significant change in drainage was observed over three days postoperatively, and the quantity decreased progressively on the third postoperative day. Comparison of age groups showed that the volume of drainage from adults was greater than that from children or adolescents in the first two postoperative days, regardless of whether the drains were inserted in the scapha or concha. No statistical differences were found on the third postoperative day. A comparison of drain types revealed no statistically significant differences between scapha and concha drains three days postoperatively. The analysis demonstrated that drainage quantity is related to disposed time and patient age, but not to microtia type. The authors recommend removal of suction drains on the third postoperative day. Moreover, individualized negative suction treatment according to age or microtia type provides a safe and consistent approach to achieving acceptable results and fewer complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".