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Record W1936663284

[General surgery in a rural hospital in the State of Quintana Roo, Mexico].

2006· article· en· W1936663284 on OpenAlexaboutno aff
Guillermo Padrón-Arredondo

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgical proceduresGeneral hospitalRetrospective cohort studyAge groupsSurgeryDemographyPediatrics
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The general surgeon maintains extraordinary validity worldwide, especially in countries like the United States, Canada, India, and continents such as Australia and Africa. In addition to their role as a general surgeon, they assist with surgical pathologies in rural areas where there is generally a lack of technology to carry out complicated procedures. Therefore, we undertook this study to determine the number and type of surgical procedures carried out in a rural hospital with three general surgeons, as well as to determine morbidity and respective mortality. METHODS: The study was retrospective and longitudinal, using descriptive statistics during a 5.5-year period. RESULTS: During the period of June 1999 to December 2004, a total of 651 (100%) surgical procedures were carried out. There were 351 males (53%) and 300 females (47%) with average age of 28.5 +/- 16.0 years. There were 408 (63%) minor surgical procedures accomplished in the operating room: 150 (45%) for females with average age of 25.8 +/- 13.8 years old and 258 (55%) for males with average age of 27.7 +/- 15.5 years old. There were 243 major surgical procedures (37%): for females there were 150 (60%) with average age of 28.4 +/- 11.8 years old and for males there were 93 (40%) with average age of 29.5 +/- 16.6 years old [morbidity, six cases (0.9%) and mortality, two cases (0.3%)]. CONCLUSIONS: The demand for surgery in rural areas is not different from the surgery carried out in large cities, although there are limitations. It is important in this regard to adequately prepare the general surgeon in Mexico.

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)0.002
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 score0.029
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.029
GPT teacher head0.335
Teacher spread0.307 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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