Management of Gynecologic Cancer During COVID-19 Pandemic: South Asian Perspective
Bibliographic record
Abstract
Management of gynecological cancers has suffered during the pandemic, partly due to lockdown and partly due to directing resources to manage COVID-19 patients. Modification of gynecological cancer management during this pandemic is recommended. Cervical cancer patients who present with stage IA1 disease can have a delay of up to 8 weeks for surgical treatment, considering the slow tumor growth rate. Women with stages IA2, IB1, IB2, IIA1 must undergo radical hysterectomy and lymphadenectomy within 6 to 8 weeks. In areas where surgical treatment is not available, patients should be referred for radiation therapy/areas with adequate surgical expertise. The surgical option is attractive for early cancers during the COVID era, as it involves a single visit compared to the multiple visits required for chemoradiation. The value of lymph node staging needs to be reconsidered. Neoadjuvant chemotherapy should be given preference over primary cytoreductive surgery for advanced ovarian cancers. Surgeries, which demand extended surgical time such as Hyperthermic Intraperitoneal Chemotherapy and pelvic exenterations, should be avoided during this pandemic. For patients scheduled for interval surgery after two or three neoadjuvant cycles, six cycles of chemotherapy should be considered before surgery is performed. For early-stage, low-grade endometrial cancer, consideration should be given to medical management until surgery is possible. The above recommendations have been made keeping in mind the geography, patient load, and availability of resources available to health care providers from southeast Asia. They might not be applicable globally and every practitioner should take call regarding patient's management as per availability of resources and loco-regional circumstances. The implementation of recommended international guidelines for the management of gynecologic cancers should take precedence. Each modification to the standard approach should be approved by a multidisciplinary team depending on the condition of the patients and the locoregional circumstances.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".