https://ecancer.org/en/journal/article/1097-radiotherapy-waiting-time-in-northern-nigeria-experience-from-a-resourcelimited-setting
Bibliographic record
Abstract
BACKGROUND: Access and availability of radiotherapy treatment is limited in most low- and middle-income countries, which leads to long waiting times and poor clinical outcomes. The aim of our study is to determine the magnitude of waiting times for radiotherapy in a resource-limited setting. METHODS: This is a retrospective cohort study of patients with the five most commonly treated cancers managed with radiotherapy between 2010 and 2014. Data includes diagnosis, patients' demographics and treatment provided. The waiting time was categorised into intervals (1) between diagnosis and first radiation consultation (2) First consultation to radiotherapy treatment (3) Decision-to-treat to treatment and (4) Diagnosis to treatment. RESULTS: A total of 258 cases were involved, including cervical (50%; 129/258), breast (27.5%; 71/258), nasopharynx (12.8%; 33/258), colorectal (5%; 13/258) and prostate cancers (4.7%; 12/258). Mean age was 48 (±12.9) years. Treatment with radical intent comprised 67% (178/258) of cases, while 33% (80/258) had palliative treatment. The median time from diagnosis to first radiation consultation was 40 (IQR 17-157.75) days for all the patients, with prostate cancer having the longest time - 305 days (IQR 41-393.8). The median time between the first radiation oncology consultations and first radiotherapy treatment was 130.5 (IQR 14-211.5) days; cervical cancer patients waited a median of 139 (IQR 13-195.5) days. The median time between diagnosis and first radiotherapy for breast cancer patients was 329 (IQR 207-464) days, compared to 213 (IQR 101.5-353.5) days for all the patients. CONCLUSION: The study shows that waiting time for radiotherapy in Nigeria was generally longer than what is recommended internationally. This reflects the need to improve access to radiotherapy in order to improve cancer treatment outcomes in resource-limited settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".