Pain management policy formulation at a tertiary care teaching institute in India: A prospective observational study
Bibliographic record
Abstract
Background: Access to pain management has been recognized as a fundamental human right. Inadequate pain relief hampers the quality of life and has a physiological and psychosocial impact on the patient and caregivers. Inadequate pain relief remains the leading cause of suffering in hospitalized patients worldwide. Objective: The objective of this article is to provide adequate pain relief to hospitalized patients through proper assessment, treatment, and monitoring of pain by the trained health-care workers through a sustainable and effective institutional pain management policy. Methods: The formulation of pain management policy at a tertiary care teaching institute was conducted in three phases - Phase 1: need assessment by an open-label, uncontrolled, prospective observational study over 1 month period, Phase 2: teaching, training, and awareness of health-care workers, and Phase 3: constitution of the committee at the institute level with the formation of pain resource teams. Results: An open-label, prospective observational study conducted over 1 month revealed that among 814 hospitalized patients, 108 out of 235 (46%) patients in medical and 385 out of 579 (66.5%) patients in the surgical cohort had NRS score of ≥3, implying an inadequate pain relief even at 24 h following medical or surgical intervention, respectively. Conclusion: The provision of effective and adequate pain relief to hospitalized patients requires trained health-care workers and a uniform and structured pain management policy at the institutional level. Recognition and addressal of the barriers and challenges while framing an institutional pain policy is of utmost importance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".