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Record W1712248222

Effect of foot reflex massage on sternotomy pain after coronary artery bypass graft surgery

2009· article· en· W1712248222 on OpenAlexaboutno aff
M Sadeghi Shermeh, Parisa Bozorgzad, A R Ghafourian, Abbas Ebadi, N Razmjouei, Afzali Mahboubeh, Azim Azizi

Bibliographic record

VenueJournal of Critical Care Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMassageAnesthesiaPlaceboFoot (prosody)ReflexSurgeryArteryPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Aims. This study was performed to investigate the effect of foot reflex massage on sternotomy pain of patients after coronary artery bypass graft surgery. Methods. In a quasi-experimental study, 90 patients were randomly divided into three groups of case, control and placebo. The reflexology group received a 10-minute right foot massage in desired location twice a day with 6 hours intervals for 2 consecutive days. The placebo group undertook a 10-minute left foot massage and the control group received no intervention. Only at mentioned times, the amount of pain was measured by McGill visual scale. Results. The mean of pain intensity before and after intervention had significant difference in three groups (p<0.001). Average of pain intensity in the case group was 6.4(±2.1) before intervention and 3.4(±5.1) after intervention. The mean of pain intensity in control group before and after intervention was respectively 5.1(±1.7) and 5(±1.9). Independent T-test showed a significant reduction in intensity of post-operative pain between case and control groups (p<0.001). Conclusion. Foot reflex massage appears to be a useful method for reducing sternotomy pain in patients after coronary artery bypass graft 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.021
GPT teacher head0.386
Teacher spread0.365 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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

Citations26
Published2009
Admission routes1
Has abstractyes

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