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Record W2774187034 · doi:10.1016/s0140-6736(17)32451-0

Atraumatic versus conventional lumbar puncture needles: a systematic review and meta-analysis

2017· review· en· W2774187034 on OpenAlexaff
Siddharth Nath, Alex Koziarz, Jetan H. Badhiwala, Waleed Alhazzani, Roman Jaeschke, Sunjay Sharma, Laura Banfield, Ashkan Shoamanesh, Sheila K. Singh, Farshad Nassiri, Wieslaw Oczkowski, Emilie P. Belley‐Côté, Ray Truant, Kesava Reddy, Maureen O. Meade, Forough Farrokhyar, Małgorzata M Bała, Fayez Alshamsi, Mette Krag, Itziar Etxeandia‐Ikobaltzeta, Regina Kunz, Osamu Nishida, Charles Matouk, Magdy Selim, Andrew Rhodes, Gregory W. J. Hawryluk, Saleh A. Almenawer

Bibliographic record

VenueThe Lancet · 2017
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsImpactHamilton Health SciencesPopulation Health Research InstituteUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineMeta-analysisLumbar punctureSurgeryRandomized controlled trialIncidence (geometry)LumbarComplicationRelative riskAnesthesiaConfidence intervalInternal medicineCerebrospinal fluid

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0160.026
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.329
GPT teacher head0.418
Teacher spread0.090 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations176
Published2017
Admission routes1
Has abstractno

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