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General anesthesia versus regional anesthesia for the management of appendicitis in a county hospital in Northern Guatemala

2023· article· en· W4366772143 on OpenAlexaff
Sergio Huerta, Nguyen TRAN, Maria Ruiz, A. Ochoa-Hernandez, Cesar ORTIZ-VARGAS, Tri PHUNG

Bibliographic record

VenueChirurgia · 2023
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsSt. Peter's Hospital
Fundersnot available
KeywordsMedicineAlvarado scoreMale genderYoung adultLocal anesthesiaAppendicitisAnorexiaPresentation (obstetrics)AnesthesiaGeneral surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Both laparoscopic and open appendectomies are performed in low- and middle-income countries (LMIC). At a county hospital in Northern Guatemala, patients with appendicitis undergo an open appendectomy typically under regional anesthesia (RA); however, some patients require general anesthesia (GA). Characteristics of patients requiring GA were analyzed.METHODS: Data on 256 appendectomies performed at the Regional Hospital of San Benito Petén in Northern Guatemala were collected and dichotomized by anesthesia type GA (N.=46 [18%]) vs. RA (N.=210 [82%]). The characteristics between these two cohorts were analyzed.RESULTS: Most patients were young adults (age 23.3±14.7 years old, 56% male, 42.2% pediatric). Gross pathology demonstrated perforated, suppurative, and gangrenous appendicitis in 54% of patients. Male gender (70.0% vs. 30.0%), age less than 18 (23.1% vs. 11.6%), tachycardia on presentation, anorexia, and the Modified Alvarado Score (MASS) were significantly associated with GA. Male gender (OR [95% CI], 2.42 [1.08 to 5.42]), age less than 18 (4.21 [1.88 to 9.42]), and MASS (1.49 [1.13 to 1.96]) were independent predictors of patients necessitating GA.CONCLUSIONS: Male, young patients <18.0 years old, and those with a modified Alvarado score >6 should be considered for GA as they are likely to fail RA.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.188
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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