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Record W1500112406 · doi:10.1002/9781444345100.ch18

Minimally Invasive Techniques in Total Hip Arthroplasty

2011· other· en· W1500112406 on OpenAlexaff
Amre Hamdi, Paul E. Beaulé

Bibliographic record

VenueEvidence-Based Orthopedics · 2011
Typeother
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineArthroplastyEnthusiasmSurgeryTotal hip arthroplastyHip arthroplastyInvasive surgeryHip replacementIntervention (counseling)Postoperative painGeneral surgeryPsychologyNursing

Abstract

fetched live from OpenAlex

Total hip arthroplasty is one the most successful surgeries in modern era because of the overall marked improvement of the patient function and quality of life1. once a surgical intervention has achieved a certain standard of efficacy and reproducibility, further developments can be placed on minimizing the morbidity of the intervention. less invasive surgical techniques as well as multimodal pain management have also evolved over the last decade in the field of joint replacements especially total hip arthroplasty enabling patients to potentially recover faster as well as optimize their overall function by avoiding excessive muscle dissection2. As with any new surgical technique, initial enthusiasm was based on high patient expectations3 as well as surgeon enthusiasm but as is all too common in surgery over enthusiasm lead to some serious complications4. In this chapter we will review the current techniques as well as clinical results and future of minimally invasive hip replacement surgery.

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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 head0.038
GPT teacher head0.278
Teacher spread0.239 · 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
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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