Teaching cervical cancer surgery in low- or middle-resource countries.
Bibliographic record
Abstract
BACKGROUND: With the widespread implementation of screening programs internationally, there will be an increase in early stage cervical cancer cases. In response to this, the Ministry of Health in each country will need to plan strategies to provide care such as radical surgery or radiation for this potentially curable group of women. METHODS: The Gynaecologic Oncologists of Canada created a teaching module to intensively train a small number of locally identified gynecologists to perform radical hysterectomy and pelvic lymphadenectomy. The process was based on adult learning principles; it involved a Canadian gynecologic oncologist working in the low- or middle-resource country with the gynecologists and problem-solving local issues in health care delivery. RESULTS: The teaching process included a pretest and a posttest on the basis of the objectives of the module. There were 7 modules including preoperative evaluation of the patient, cone biopsy, radical hysterectomy, pelvic lymphadenectomy, ureteric injury, vascular injury, and follow-up after surgery. Each module was divided into background information, techniques, and complications. There were video clips imbedded in the modules. After the educational modules had been reviewed, the learners were walked through the surgical procedures repeatedly including a detailed assessment of performance after each case. Participants had the opportunity to provide feedback on the training program. The module was reviewed in Mongolia and implemented in Kenya. CONCLUSIONS: In low- and middle-resource countries where there is an urgent need to provide a curative surgical option for the management of early cervical cancer, a focused high-intensity curriculum delivered by a trained surgeon can translate into immediate change in clinical and surgical practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".