Expanding Access to Computed Tomographic Staging and Three-Dimensional Intensity Modulated Radiotherapy for Cervical Cancer in Ghana
Bibliographic record
Abstract
PURPOSE: To build capacity for improved treatment of locally advanced cervical cancer in Ghana, including computed tomography (CT) staging and intensity modulated radiotherapy (IMRT). MATERIALS AND METHODS: Patients with histologically confirmed cervical cancer were prospectively staged with abdominopelvic CT and ultrasound and offered the opportunity to have IMRT instead of conventional two-dimensional radiotherapy. The development of an efficient, high-quality, and safe IMRT program was facilitated by investment in new technology and comprehensive training of the interdisciplinary radiotherapy team in collaboration with a North American center of excellence. RESULTS: Of 215 patients with cervical cancer referred in 2022, 66% were able to afford CT scans and 26% were able to afford IMRT. Lymph node metastases were identified in 52% of patients by CT but in only 2% of patients by ultrasound. The use of CT resulted in 63% of patients being upstaged and changed treatment intent or radiation treatment volumes in 67% of patients. Patients who had IMRT experienced fewer acute side effects and were more likely to complete treatment as planned. CONCLUSION: It is feasible to provide state-of the-art cancer treatment with CT staging and IMRT to patients with cervical cancer in low-resource settings and achieve meaningful improvements in outcomes. It requires a broad commitment by program leadership to invest in technology and staff training. Major challenges include balancing improved clinical care with reduced patient throughput when radiation treatment capacity is constrained, and with the additional cost in the absence of universal health coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".