Glove and Instrument Changing to Prevent Tumour Seeding in Cancer Surgery: A Survey of Surgeons’ Beliefs and Practices
Bibliographic record
Abstract
Background: Some surgeons change gloves and instruments after the extirpative phase of cancer surgery with the intent of reducing the risk of local and wound recurrence. Although this practice is conceptually appealing, the evidence that gloves or instruments act as vectors of cancer-cell seeding in the clinical setting is weak. To determine the potential effect of further investigation of this question, we surveyed the practices and beliefs of a broad spectrum of surgeons who operate on cancer patients. Methods: Using a modified Dillman approach, a survey was mailed to all 945 general surgeons listed in the College of Physicians and Surgeons of Ontario public registry. The survey consisted of multiple-choice and free-text response questions. Responses were tabulated and grouped into themes, including specific intraoperative events and surgeon training. Predictive variables were analyzed by chi-square test. Results: Of 459 surveys returned (adjusted response rate: 46%), 351 met the inclusion criteria for retention. Of those respondents, 52% reported that they change gloves during cancer resections with the intent of decreasing the risk of tumour seeding, and 40%, that they change instruments for that purpose. The proportion of respondents indicating that they take measures to protect the wound was 73% for laparoscopic cancer resections and 31% for open resections. Training and years in practice predicted some of the foregoing behaviours. The most commonly cited basis for adopting specific strategies to prevent tumour seeding was "gut feeling," followed by clinical training. Most respondents believe that it is possible or probable that surgical gloves or instruments harbour malignant cells, but that a cancer recurrence proceeding from that situation is unlikely. Conclusions: There is no consensus on how gloves and instruments should be handled in cancer operations. Further investigation is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".