Cancer pain control in a Nigerian Oncology Clinic: treating the disease and not the patient
Bibliographic record
Abstract
INTRODUCTION: inadequate pain control negatively impacts the quality of life of patients with cancer while potentially affecting the outcome. Proper pain evaluation and management are therefore considered an important treatment goal. This study assessed the prevalence of pain, the prescribing patterns, and the efficacy of pain control measures in cancer patients at the Radiation Oncology Unit of the Lagos University Teaching Hospital, Lagos. METHODS: this was a longitudinal study design recruiting adults attending outpatient clinics. Participants were assessed at initial contact and again following six weeks using the Universal Pain Assessment Tool developed by the UCLA Department of Anaesthesiology. RESULTS: among the patients reviewed, 34.0% (118 of 347) were at the clinic, referred for initial assessment following primary diagnosis. All respondents had solid tumours; the most common was breast cancer. The prevalence of pain at initial assessment was 85.9% (298 of 347), with over half of respondents, 74.5% (222 of 347) characterising their pain as moderate to severe. Over a quarter, 28.9% (100 of 347) of patients were not asked about their pain by attending physicians, and none of the patients had a pain assessment tool used during evaluation. In 14.4% (43 of 298) of patients, no intervention was received despite the presence of pain. At six weeks review, 31.5% (94 of 298) of patients had obtained no pain relief despite instituted measures. CONCLUSION: under-treatment of cancer pain remains a significant weak link in cancer care in (Low-to-middle-income country) LMICs like Nigeria, with a significant contributor being physician under-evaluation and under-treatment of pain. To ensure pain eradication, the treatment process must begin with a thorough evaluation of the patient's pain, an explicit pain control goal and regular reevaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".