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Record W2158026111

Outcomes after vacuum-assisted deliveries. Births attended by community family practitioners.

2004· article· en· W2158026111 on OpenAlexaff
Colin Yarrow, Michael Klein

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsKelowna General Hospital
Fundersnot available
KeywordsMedicineForcepsVacuum extractorObstetricsMaternal morbidityCommunity hospitalPregnancySurgeryNursing
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess success rates, modes of delivery following failure, complications of mothers and newborns, and effect of extractor station and parity on vacuum-assisted deliveries attended by family physicians. DESIGN: Retrospective audit. SETTING: Community hospital. PARTICIPANTS: Thirty-five family physicians providing maternity care. MAIN OUTCOME MEASURES: Complications, parity, and extractor station of 153 vacuum-assisted deliveries from April 1, 2000, to March 31, 2003. RESULTS: Family physicians attempted 153 vacuum deliveries (82 at low station, 71 at outlet station) and had a 94.1% success rate. Of nine failed vacuum deliveries (eight at low station and one at outlet station), four were subsequently delivered by forceps and five by cesarean section. Except for one case of subdural hematoma, complications were few. Nulliparity was associated with six of the nine failed vacuum deliveries. CONCLUSION: Family physicians were usually successful with vacuum-assisted deliveries. Complications were infrequent and rapidly resolved, but one failure, which was followed by a failed forceps delivery and eventual cesarean section, resulted in a serious complication. Low station and nulliparity were associated with failure of vacuum-assisted deliveries.

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)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.305
Teacher spread0.255 · 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 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
Published2004
Admission routes1
Has abstractyes

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