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Record W4385463859 · doi:10.21608/ejhm.2023.309950

Postoperative Pain Control in Patients Undergoing Open Inguinal Hernia: Review Article

2023· article· en· W4385463859 on OpenAlexaboutno aff

Bibliographic record

VenueThe Egyptian Journal of Hospital Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInguinal herniaPain controlSurgeryGeneral surgeryPostoperative painHernia

Abstract

fetched live from OpenAlex

Background: Nociceptive and neuropathic post-operative discomfort of moderate severity is related to inguinal hernia operations. The Wong-Baker FACES pain rating scale, the McGill Pain Questionnaire (MPQ), the Visual Analogue Scale (VAS), and the Numeric Rating Scale (NRS) for pain are all used to measure pain. Objective: To control the postoperative pain in patients undergoing open inguinal hernia. Methods: Pain Control, Undergoing Open Inguinal Hernia and Visual Analogue Scale were searched for in PubMed, Google Scholar, and The Egyptian Knowledge Bank. Systemic analgesic methods (such as opioids and nonsteroidal anti-inflammatory drugs), localised analgesic methods (such as quadratus lumborum block (QLB)), and a multimodal approach to perioperative recovery were used to control postoperative pain. High postoperative pain scores were seen in patients with high pain levels in the first week following surgery, patients who had recurrent hernia repairs, patients who had high levels of pain prior to surgery, and patients who had outpatient surgery were all risk factors for inguinal hernia postoperative pain. Only the most current or comprehensive studies were included after the authors thoroughly filtered references from the pertinent literature, which comprised all the recognised studies and reviews. Conclusion: After open inguinal hernia surgery, a multimodal analgesic strategy (a mix of localised and systemic analgesia) is particularly successful at reducing postoperative discomfort and promoting early mobilisation.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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