Estimation of Annual Need, Production and Per Capita Consumptions of Oral Morphine in Bangladesh
Bibliographic record
Abstract
Background: Worldwide, opioid analgesics prescribed for the management of moderate to severe pain in both malignant and non-malignant patients. Use of opioids for pain management is very limited in healthcare facilities of Bangladesh. For ensuring proper management this study aims to explore opioid consumption, accessibility and availability especially in cancer pain in Bangladesh.Methods: This observational study used preconstructed questionnaire to estimate annual production and use of opioids at different settings. Information was obtained from annual drug report of 2020-2021 Bangladesh’s Department of Narcotics Control (DNC), Director General of Health Services of Bangladesh and three pharmaceutical companies. Availability and accessibility of morphine was explored among the tertiary care centre of Bangladesh using patient’s recordsResults: Morphine and other 5 types of opioids are available in different formulations in Bangladesh. Locally only two pharmaceutical companies producing morphine though there are 3 are licensed. In last 5 years on an average 15.34 kg (range 10.9 kg – 20.1 kg) morphine produced. There are only a few hospitals where oral morphine is readily available. But almost all of them are metropolitan Dhaka based.Conclusion: The low consumption of morphine indicates the poor pain management scenario of the country. Pain and palliative care professionals in Bangladesh continue to advocate for improvements which will ensure that opioids are available, accessible and affordable for all patients in Bangladesh.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".