Optimal management of esophageal cancer in Africa: A systemic review of treatment strategies
Bibliographic record
Abstract
Esophageal cancer (EC) is a leading cause of cancer morbidity and mortality in Africa. Despite the high burden of disease, optimal management strategies for EC in resource-constrained settings have yet to be established. This systematic review evaluates the literature on treatments for EC throughout Africa and compares the efficacy and safety of varying treatment strategies in this context (PROSPERO CRD42017071546). PubMed, Embase and African Index Medicus were searched for studies published on treatment strategies for EC in Africa from 1980 to 2020. Searches were supplemented by examining bibliographies of included studies and relevant conference proceedings. Methodological quality/risk of bias was assessed using the Cochrane Risk-of-Bias tool and the Newcastle-Ottawa Scale. Forty-six studies were included. Case series constituted the majority of studies: 13 were case series reporting on outcomes of esophagectomies, 17 on palliative luminal or surgical interventions, four on radiotherapy and three on concurrent chemoradiation. Nine randomized controlled trials were identified, of which four prospectively compared different treatment modalities (one investigating radiotherapy vs chemoradiation, three evaluating rigid plastic stents vs other treatments). This review summarizes the research on EC treatments in Africa published over the last four decades and outlines critical gaps in knowledge related to management in this context. Areas in need of further research include (a) evaluation of the safety and efficacy of neoadjuvant therapy in patients with locally advanced disease; (b) strategies to improve long-term survival in patients treated with definitive chemoradiation; and (c) the comparative effectiveness of modern palliative interventions, focusing on quality of life and survival as outcome measures.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".