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Record W2897429648 · doi:10.21037/jtd.2018.09.112

Pain management within an enhanced recovery program after thoracic surgery

2018· review· en· W2897429648 on OpenAlexaff
Calvin Thompson, Daniel French, I. Costache

Bibliographic record

VenueJournal of Thoracic Disease · 2018
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicinePostoperative painPain managementPerioperativePain controlCardiothoracic surgeryChronic painSurgeryInvasive surgeryAnesthesiaPhysical therapy

Abstract

fetched live from OpenAlex

Evidence for ERAS within thoracic surgery (ERATS) is building. The key to enabling early recovery and ambulation is ensuring that postoperative pain is well controlled. Surgery on the chest is considered to be one of the most painful of surgical procedures for both open and minimally invasive surgery (MIS) approaches. Increasing use of MIS and improved perioperative care pathways has resulted in shorter length of stay (LOS), requiring patients to achieve optimal pain control earlier and meet discharge criteria sooner, sometimes on the same day as surgery. This requires optimizing pain control earlier in the postoperative recovery phase in order to enable ambulation and a better recovery profile, as well as to minimize the risk for development of chronic persistent postoperative pain (CPPP). This review will focus on the options for pain management protocols within an ERAS program for thoracic surgery patients (ERATS).

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)0.007
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.383
Teacher spread0.339 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations68
Published2018
Admission routes1
Has abstractyes

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