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Record W3188271350 · doi:10.32920/ihtp.v1i2.1449

Tackling pain after cardiac surgery: It takes a village!

2021· article· en· W3188271350 on OpenAlexvenueno aff
Géraldine Martorella

Bibliographic record

VenueInternational Health Trends and Perspectives · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiac surgeryPsychological interventionOpioidChronic painQuality of life (healthcare)Intensive care medicineAnesthesiaPhysical therapySurgeryNursingInternal medicine

Abstract

fetched live from OpenAlex

There is increasing concern regarding the risk to develop chronic pain after cardiac surgery with potential detrimental effects on recovery and quality of life. With shortened hospital stays after cardiac surgery, there needs to be more emphasis placed on self-management skills and the support provided to patients and their informal caregivers during the subacute phase. A paradigm shift needs to occur on multiple levels to prevent chronic pain and opioid misuse after surgery. Initiating this change means redefining the timing, recipients, and content and format of interventions. Several avenues can be examined and translated in practice to promote a successful transition after cardiac 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)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0080.013
Open science0.0010.006
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0260.008

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 head0.027
GPT teacher head0.324
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2021
Admission routes1
Has abstractyes

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