MétaCan
Menu
Back to cohort
Record W2032474797 · doi:10.1159/000252957

Outpatient Hysteroscopy with Combined Local Intracervical and Intrauterine Anesthesia

2009· article· en· W2032474797 on OpenAlexaff
Mohammed Agdi, Togas Tulandi

Bibliographic record

VenueGynecologic and Obstetric Investigation · 2009
Typearticle
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsHysteroscopyMedicineLocal anesthesiaAnesthesiaObstetrics

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: To evaluate the degree of pain during and after office hysteroscopy with combined intracervical and intrauterine anesthesia compared to intracervical anesthesia only. METHODS: We evaluated the amount of pain experienced during office hysteroscopy using combined local intracervical and intrauterine anesthesia, 10, 30, and 60 min after, and during endometrial biopsy prospectively in 37 infertile women (study group). We used a visual analog scale ranging from 0 to 10. Seventy-six women who received only intracervical anesthesia served as historical controls. RESULTS: The mean ages of patients in the control and study groups were comparable. Patients' perception of pain was significantly higher during endometrial biopsy than during or after hysteroscopy in the study patients (p < 0.01, 95% CI 0-3). The mean pain score in the control group was significantly higher than that in the study group during hysteroscopy (3.3 +/- 0.2 vs. 2.2 +/- 0.3; p < 0.05, 95% CI 0-2). However, there was no significant difference in the pain scores between the control and study groups during endometrial biopsy and 10, 30, and 60 min after the procedure. CONCLUSION: Endometrial biopsy is associated with more pain than office hysteroscopy. Additional intrauterine anesthesia with 1% lidocaine significantly reduces pain sensation during office hysteroscopy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations8
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueGynecologic and Obstetric InvestigationSame topicGynecological conditions and treatmentsFrench-language works237,207