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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 OpenAlex
Alexander Huang, Abid Azam, Shira C. Segal, Kevin Pivovarov, Gali Katznelson, Salima Ladak, Alex Mu, Aliza Weinrib, Joel Katz, Hance Clarke

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 head0.019
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