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Record W2621381022 · doi:10.1097/scs.0000000000003708

Analyzing the Cost of Autogenous Cranioplasty Versus Custom-Made Patient-Specific Alloplastic Cranioplasty

2017· article· en· W2621381022 on OpenAlexaffabout
Mohamed Amir Mrad, Khalid Murrad, Oleh Antonyshyn

Bibliographic record

VenueJournal of Craniofacial Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsCranioplastyMedicineSurgerySkull

Abstract

fetched live from OpenAlex

PURPOSE: Comparing expenses related to autogenous cranial vault reconstruction versus custom-made patient-specific alloplastic cranioplasty. METHODS: The authors retrospectively reviewed charts of a group of patients who underwent autogenous cranioplasty and poly-ether-ether ketone (PEEK) cranioplasty. The data collected from the patient files included demographic information, details of the surgery, postoperative recovery data, and also duration of surgery. The authors also added costs related to the length of surgery, utilization of intensive care unit, length of hospital stay, amount and seriousness of complications, and hardware cost. The outcomes were studied in terms of skull form maintenance and complications.Eleven of our patients had PEEK cranioplasty at Sunnybrook Hospital, Toronto, ON, in the period from July 2009 to June 2011. The authors identified 11 patients who had split skull autogenous bone graft cranioplasty. They were matched for age and skull defect size.Comparable information was collected for both patient groups. The information was examined to compare costs of custom-made patient-specific alloplastic implants and costs of autogenous cranioplasty. RESULTS: Conclusions made from this paper will hopefully serve as guidance for allocation of hospital funding and resources at the Ministry of Health level.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.283
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2017
Admission routes2
Has abstractyes

Explore more

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