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Computer-assisted Trauma Surgery

2010· review· en· W2315912503 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2010
Typereview
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineFluoroscopyTraumatologyIntramedullary rodPercutaneousFixation (population genetics)SurgerySurgical planningMedical physicsRadiation exposureOrthopedic surgeryRadiologyNuclear medicine

Abstract

fetched live from OpenAlex

Computer-assisted orthopaedic surgery (CAOS) is performed by digitizing the patient's anatomy, combining the images in a computerized system, and integrating the surgical instruments into the digitized image background. This allows the surgeon to navigate the surgical instruments and the bone in an improved, virtual visual environment. CAOS in traumatology is performed with images obtained by fluoroscopy, CT, or three-dimensional fluoroscopy. CAOS is used in basic trauma procedures for preoperative planning, fracture reduction, intramedullary nailing, percutaneous screw or plate fixation, and hardware or shrapnel removal. Potential benefits of CAOS include minimal invasiveness, increased accuracy, and decreased radiation exposure. Limitations include a significant learning curve, increased surgical time, requirements for special setup and equipment handling in the operating room, specialized technical support, and cost. Current evidence shows no advantage with CAOS in trauma cases compared with conventional methods. Prospective randomized trials and clinical outcomes are lacking.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.346
Teacher spread0.293 · 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