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Record W4319161926 · doi:10.31219/osf.io/pmbdw

Outcome reporting after inguinal hernia repair in children: A systematic review towards a core outcome set

2023· review· en· W4319161926 on OpenAlexaff
Sanne C Maat, Laurens D. Schattenkerk Eeftinck, Faridi s. van Etten-Jamaludin, Nancy J. Butcher, Martin Offringa, Ramon R. Gorter, Joep. P.M. Derikx

Bibliographic record

Venuenot available
Typereview
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsInguinal herniaMedicineRandomized controlled trialCochrane LibraryMEDLINESystematic reviewOutcome (game theory)Meta-analysisHerniaCochrane collaborationGeneral surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: A Core Outcome Set for Inguinal Hernia repair (COS-IH) will facilitate adequate comparison of treatment strategies and the interpretation and implementation of clinical trial results. To develop such a COS-IH, this systematic review is performed to determine which outcomes are reported in RCTs and MA in children aged 0-16 years undergoing inguinal hernia repair. Methods: A systematic review was performed according to the PRISMA guidelines using PubMed, EMBASE, MEDLINE and the Cochrane Library databases (Prospero registry: CRD42021281422). This systematic review included all randomized trials (RCTs) and meta-analysis (MA) that assessed inguinal hernia repair in children. Results: A total of 96 unique outcomes were identified in the 47 included articles. These outcomes were mapped into the 62 terms as shown in table 2. Conclusion: The development of this IH-COS is crucial for outcome data comparison and will enable adequate and efficient comparison of treatment strategies, and will aid the interpretation and implementation of clinical trial results.

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)0.035
metaresearch head score (Gemma)0.050
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.577
GPT teacher head0.603
Teacher spread0.026 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
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

Citations1
Published2023
Admission routes1
Has abstractyes

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