Moving forward through consensus: a national Delphi approach to determine the top research priorities in prostate cancer in Uganda
Bibliographic record
Abstract
OBJECTIVE: To identify key areas for research in prostate cancer (PC) in the Ugandan context by establishing the major health system, socioeconomic and clinical barriers to seeking, reaching and receiving high-quality cancer care. DESIGN: Modified Delphi Technique. SETTING: Government and private-not-for-profit hospitals. METHODS: We applied a two-stage modified Delphi technique to identify the consensus view across cancer experts. In round 1, experts received a questionnaire containing 21 statements drawn from a systematic review identifying the reason for the delay in accessing cancer care. Each statement was scored out of 20. Statements scoring ≥15 from over 70% of participants were prioritised for inclusion while statements for which <30% of participants gave a score of ≥15 were excluded. Sixteen statements were included in round 2 as they did not receive consensus for inclusion or exclusion. RESULTS: We found that the top six research priority areas arise from challenges including: (1) lack of diagnostic services-ultrasound, laboratory tests and biopsy facilities; (2) high costs of services, for example, surgery, radiotherapy, hormone therapy are unaffordable to most patients, (3) lack of essential medicines, (4) limited radiotherapy capacity, (5) lack of awareness of cancer as a disease and low recognition of symptoms, (6) low healthcare literacy. The lack of critical surgical supplies, high diagnostic and treatment costs were ranked highest in order of importance in round 1. Round 2 also revealed lack of diagnostic services, unavailability of critical medicines, lack of radiotherapy options, high costs of treatments and lack of critical surgical supplies as the top priorities. CONCLUSION: These research priority areas ought to be addressed in future research to improve prompt PC diagnosis and care in Uganda. There is need to improve the supply of high-quality affordable anticancer medicines for PC patients so as to improve the survivorship from the cancer.
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 | 0.192 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".