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Record W2420974926 · doi:10.1177/194589240501900218

The Effects of an External Nasal Dilator on Labor

2005· article· en· W2420974926 on OpenAlexaff
Oscar Sadan, Sagit Shushan, Ido Eldar, Shmuel Evron, Samuel Lurie, Mona Boaz, Marek Glazerman, Yehudah Roth

Bibliographic record

VenueAmerican Journal of Rhinology · 2005
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDilatorPlaceboAmnioinfusionAnesthesiaAmniotic fluidLabor inductionPregnancyObstetricsFetusSurgeryOxytocinInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to assess the effect of an external nasal dilator on several variables characterizing labor in both mother and fetus. METHODS: One hundred and fifty primigravida women in active labor were randomized to wear, throughout labor, either a dilator spring-loaded nasal strip or a placebo device. Data were obtained during labor and compared between the groups. After delivery, the satisfaction rate was assessed. RESULTS: No differences were found between the study and the control group regarding rate of induction or augmentation of labor as well as Montevideo units reached, frequency of rupture of membranes, duration of the active phase and second stage of labor, usage of epidural analgesia, normal fetal heart pattern, meconium-stained amniotic fluid, and neonatal well being. Length of maternal and neonatal hospitalization also did not differ between the groups. Satisfaction rate was significantly higher in parturient women wearing nasal strips with a dilator spring than in parturient women wearing a placebo spring (P < 0.0001). CONCLUSION: Nasal strips do not change the course but ameliorate the quality of labor by improving the ease of breathing. Nasal dilators sustain the respiratory effort associated with the long process of labor and may control the switch from nasal to oronasal breathing during delivery.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.176

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 studies0.0000.000
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 head0.008
GPT teacher head0.323
Teacher spread0.315 · 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 designOther design
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

Citations10
Published2005
Admission routes1
Has abstractyes

Explore more

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