Friday December 13, 2019

Treatment for lower back pain poor, harmful globally: Lancet

Current treatments including opioids, injections and surgery to treat lower back pain -- the leading cause of disability globally

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Researchers study patterns of back pain. IANS

Current treatments including opioids, injections and surgery to treat lower back pain — the leading cause of disability globally — are useless, unnecessary and harmful, finds a series of studies in The Lancet. Globally, lower back pain affects more than 540 million people and the condition has doubled in the last 25 years.

The prevalence of the condition is expected to continue to increase with an ageing and increasingly obese population. Medical care with inappropriately high use of imaging, rest, opioids, spinal injections, and surgery is making the problem worse in both developed and developing countries, the findings showed.

desk job
Sitting on your work-desk for extended hours may be the reason for the shooting pain in your back and body. Pixabay

“The burden from low back pain has reached a tipping point where the condition is growing rapidly, is poorly understood and is being mismanaged medically — at cost both to the patient and to the healthcare system,” said Rachelle Buchbinder, Professor at the Monash University in Melbourne.

“Low-and middle-income countries are already emulating the low-value care that is endemic in high-income countries. “Across the globe (there is) inappropriately high use of imaging, rest, opioids, spinal injections and surgery. Doing more of the same will not reduce low back pain disability nor its long term consequences,” Buchbinder said.

People with physically demanding jobs, physical and mental comorbidities, smokers, and obese individuals are at greatest risk of reporting low back pain.

Also Read: Lower Back pain reduced by muscle exercise

The researchers call for a coordinated international leadership to drive transformational change across health and social services and occupational settings to stop fragmented and outdated models of care. They also call for avoidance of the harmful and useless medical treatments through the adoption of a similar framework to drug regulation.

Public health campaigns need to address the widespread population and health professional misconceptions about the causes and prognosis of low back pain and the effectiveness of different treatments. IANS

Next Story

Machine Learning Can Help Doctors to Improve End-Of-Life Conversation with Patients

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families

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Machine Learning
A Research used Machine Learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients. Pixabay

Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.

Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.

Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.

“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.

Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.

They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.

“We picked up some strong signals,” said Gramling.

Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.

Machine Learning
Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication. Pixabay

The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.

“What we found supports the importance of narrative in medicine,” he said.

That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.

ALSO READ: Light Alcohol Consumption Might Also Increase Cancer Risk: Study

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families. (IANS)