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Record W4414625029

The History of Anesthesia and Anesthesiologists in Iran.

2024· article· en· W4414625029 on OpenAlexaff
Alireza Salimi, Makan Sadr, Babak Daneshfard

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsCanadian Journal of Administrative Sciences
Fundersnot available
KeywordsAnesthesiologyPain medicineIntervention (counseling)Nurse anesthetistIntensive careRegional anesthesia
DOInot available

Abstract

fetched live from OpenAlex

The evolution of anesthesia and anesthesiology represents a pivotal chapter in the history of medical science, significantly enhancing patient care and surgical outcomes. General anesthesia, defined as the deliberate induction of a temporary state of pain relief, muscle paralysis, memory impairment, and unconsciousness, has revolutionized medical procedures by inhibiting the normal functioning of the central nervous system. In Iran, the journey of anesthesiology began with early contributions from ancient scholars and practitioners, whose innovative techniques laid the groundwork for future advancements. The field witnessed significant progress in the mid-20th century, aligning with global developments in medical science. Initially focused on intraoperative care, anesthesiology in Iran expanded to encompass preoperative evaluations, postoperative assessments, and comprehensive patient monitoring, addressing complications related to surgery and anesthesia. In addition to analyzing the evolution of anesthesiology from solely surgical intervention to a broader field encompassing preoperative evaluation, postoperative care, and critical care management, this paper addresses the challenges and opportunities facing anesthesia and anesthesiology in Iran, including the need for wider access to safe and reliable services and the integration of advanced technologies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.057
GPT teacher head0.203
Teacher spread0.146 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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