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Record W2171975104 · doi:10.4021/jnr.v1i5.78

The Efficiency of Intravenous Theophylline on the Headache Which Occurs After Spinal Anesthesia

2011· article· en· W2171975104 on OpenAlexvenueno aff
Hakan Akdere, Kamil Mehmet Burgazli

Bibliographic record

VenueJournal of Neurology Research · 2011
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaTheophyllineCerebrospinal fluidPlaceboInternal medicine

Abstract

fetched live from OpenAlex

Background : Headache which occurs after spinal anesthesia is the most frequent complication (30 - 40%). In our study, we studied the efficiency of intravenous theophylline application on the patients who came with after spinal anesthesia headache (ASAH).  Methods : Sixty patients were included in this study. The patients were divided into three groups. The first group was gi ven; intravenous (iv)200 mg theophylline + 1000 ml Dextrose Linger lactate (DRL) with in half hour, the 2nd, oral 100 mgcaffeine + 500 mg paracetomol + 1000 ml DRL and the 3rd, oral and iv placebo with 1000 ml DRL within half hour. These patients were asked to degree the pain before the medicine was given and one hour after it was given using The Visual Pain Scala (VPS).  Results : In the first group, statistically significant difference was detected in the VPS results before and after the theophylline infusion (P 0.001). While the average VPS result was (7.34 ± 2.15) before, and (2.2 ± 2.04) after the infusion. In the second and third group, no statistically significant difference was detected in VPS (P > 0.1).  Conclusion : The reason for the pain is the leak of the cerebrospinal fluid (CSF) from where the injection is given and the fall of CSF pressure. The agents like theophylline which causes vasoconstriction in brain veins help get over the headache. Our results tend to show that theophylline infusion after spinal anesthesia is a method which is efficient, fast and does minimal harm during the headache therapy. doi:10.4021/jnr71e

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.116
GPT teacher head0.368
Teacher spread0.252 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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