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Record W2157847094 · doi:10.1177/1089253210378401

Pain Management After Cardiac Surgery

2010· article· en· W2157847094 on OpenAlex
Jennifer Cogan

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.

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2010
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineCardiac surgeryChronic painIncidence (geometry)Depression (economics)AnesthesiaSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Pain levels after cardiac surgery are often severe and undertreated. The effects of undertreatment may be both severe and prolonged. The incidence of chronic pain after cardiac surgery varies between 21% and 55%. Pain syndromes that occur following cardiac surgery may be multiple and may be of visceral, musculoskeletal, or neurogenic origin. Risk factors for acute pain vary depending on the study but generally include younger age, longer duration of surgery, and the location of the surgery. Risk factors for chronic pain include depression and psychological vulnerability, both preoperative and postoperative. Other independent risk factors for chronic pain are more extensive surgery, surgery lasting longer than 3 hours, and ASA grade greater than III. Pain control is achieved with regular and systematic evaluation and the use of multimodal regimens. Treatment strategies that are commonly used include opioids, paracetamol, NSAIDS, and more recently anticonvulsants.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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