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Record W2474458780 · doi:10.2217/pmt-2016-0004

Chronic Postsurgical Pain and Persistent Opioid Use Following Surgery: The Need For A Transitional Pain Service

2016· article· en· W2474458780 on OpenAlexafffundabout
Alexander Huang, Abid Azam, Shira C. Segal, Kevin Pivovarov, Gali Katznelson, Salima Ladak, Alex Mu, Aliza Weinrib, Joel Katz, Hance Clarke

Bibliographic record

VenuePain Management · 2016
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsToronto General HospitalYork UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsMedicineOpioidChronic painPain managementAnesthesiaSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

AIM: To identify the 3-month incidence of chronic postsurgical pain and long-term opioid use in patients at the Toronto General Hospital. METHODS: 200 consecutive patients presenting for elective major surgery completed standardized questionnaires by telephone at 3 months after surgery. RESULTS: 51 patients reported a preoperative chronic pain condition, with 12 taking opioids preoperatively. 3 months after surgery 35% of patients reported having surgical site pain and 13.5% continued to use opioids for postsurgical pain relief. Postoperative opioid use was associated with interference with walking and work, and lower mood. CONCLUSION: Chronic postsurgical pain and ongoing opioid use are concerns that warrant the implementation of a Transitional Pain Service to modify the pain trajectories and enable effective opioid weaning following major surgery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations114
Published2016
Admission routes3
Has abstractyes

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