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19 A tale of ill-managed pain and the opioid epidemic

2019· article· en· W2980594484 on OpenAlexaff
John S. Mikhaeil, Alexander Huang, Hance Clarke, Marcin Wąsowicz

Bibliographic record

VenuePoster presentations · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsOxycodoneMedicineOpioidMedical prescriptionChronic painCancer painHeroinAddictionPsychiatryAlternative medicinePharmacologyDrug

Abstract

fetched live from OpenAlex

The use of opioids to treat chronic non-cancer pain has increased dramatically in the last decade. Simultaneously, this surge in prescribing practices has been correlated to an almost four-fold increase in opioid-related deaths. Despite recent efforts to control the distribution of opioids and the introduction of abuse-deterrent opioid formulations, the epidemic is still yet to be fully targeted with a multi-factorial approach. In order to better understand how this crisis exists, it is important to examine patterns beginning in the 1980s. At that time, pharmaceutical companies marketed the use of opioids to treat pain and assured clinicians that the addiction profile was low, but these claims were not based on evidence-based medicine. Within a few years, the same companies promoted these drugs for use in long-term non-cancer pain, despite the lack of good evidence. Regardless, this ‘mentality’ was adapted by clinicians based on the pharmaceutical companies’ misrepresentation, which sparked the first of three major waves of increased opioid prescribing. The number of opioid prescriptions increased among primary care clinics and hospitals, and an increased amount of opioid diversion took place. Pharmaceutical companies responded to the public outcry by developing abuse-deterrent formulations such as an extended release oxycodone (OxyContin). These prescribing practices led to increased rates of opioid over-sedation and opioid-related deaths. The second wave began around 2010, as efforts were made to decrease opioid prescribing, leading to the increased popularity of a cheap, widely available option – heroin. Finally, the last resurgence was seen in 2013 due to newer synthetic opioids such as fentanyl. When a patient undergoes a surgical procedure, some level of pain is anticipated post-operatively, and the level of severity and duration varies patient-to-patient. Indirectly related to the illicit opioid epidemic, opioid-prescribing in the postoperative period has increased dramatically, feeding yet another epidemic. Although opioids are often prescribed to address the need for pain management, this is often done without a long-term plan to eventually wean off these medications. As a result, patients are ultimately left with inappropriately managed chronic pain and almost half of these patients continue to use opioids as more of a ‘band-aid’ solution. The issue becomes more complex as these patients seek care from their primary care physicians who may not have experience in weaning patients off opioids. Although some success has been seen with the implementation of prescribing guidelines and educational interventions, new programs need to be implemented to address this systemic issue. This led to the concept of a Transitional Pain Service with a focus on high-risk patients with complex pain management and opioid weaning. This is managed through an interdisciplinary team which is led by anesthesiologists with the goal of minimizing the risk of developing chronic pain postoperatively and long-term opioid use. Through the Transitional Pain Service, patient’s pain management needs are being addressed through a multidisciplinary model and programs are being implemented to combat the growing opioid epidemic that stemmed from a lack of evidence-based medicine.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.291
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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