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Record W2795773046 · doi:10.1016/j.amsu.2018.03.043

Primary total hip arthroplasty: Staying out of trouble intraoperatively

2018· review· en· W2795773046 on OpenAlexaff
Shahryar Noordin, Riaz Hussain Lakdawala, Bassam A. Masri

Bibliographic record

VenueAnnals of Medicine and Surgery · 2018
Typereview
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineImpactionOsseointegrationSurgeryTotal hip arthroplastyImplantFixation (population genetics)

Abstract

fetched live from OpenAlex

Total hip Arthroplasty is one of the most successful and commonly performed procedures in Orthopaedic Surgery. Meticulous preoperative planning allows to surgeon anticipate potential problems to reduce postoperative complications and optimize patient outcomes. Currently, the posterior approach is the most common approach utilized in the United States. In order to prevent errors in stem version, especially with cementless fixation, the entry point should be posterior, and the initial entry point should be aligned in the correct anteversion as this will determine the ultimate version of the stem. Preoperative evaluation should asses for true and apparent lengths. For successful osseointegration of cementless components, bone ingrowth occurs when there is less than 40 μm of motion between the implant and bone. Certain socket conformations such as elliptical versus hemispherical are more prone to generate fractures at the time of impaction.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.176
GPT teacher head0.374
Teacher spread0.198 · 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 designNot applicable
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

Citations11
Published2018
Admission routes1
Has abstractyes

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