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Record W2153332846 · doi:10.2217/pmt.15.7

Ensuring Safe Prescribing of Controlled Substances for Pain Following Surgery by Developing a Transitional Pain Service

2015· article· en· W2153332846 on OpenAlex
Alexander Huang, 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.

Bibliographic record

VenuePain Management · 2015
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto General HospitalYork UniversityUniversity of Toronto
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicinePain managementService (business)Postoperative painAnesthesiaIntensive care medicineMarketing

Abstract

fetched live from OpenAlex

Chronic postsurgical pain is a significant complication following major surgery, which impairs patient's quality of life. Opioid medications are the mainstay of most postoperative analgesic regimens. Growing evidence suggests inherent risks associated with opioids used for postoperative pain. Beyond common opioid-related side effects, increased mortality in the community and developing persistent opioid problems have been reported. There is a paucity of literature regarding the safe and effective management of postoperative pain as patients transition from the hospital to home/community. The introduction of a transitional pain service, whose aim is to optimize pain control, monitor and appropriately wean patients off opioid medications, prevent unnecessary readmissions post-discharge, and reduce disability associated with the development of chronic post surgical pain, will be of benefit to patients and the healthcare system.

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)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.257
Teacher spread0.214 · 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