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Record W2414252109

Cost-effectiveness of simultaneous versus sequential surgery in head and neck reconstruction.

2011· article· en· W2414252109 on OpenAlexaff
Kevin Wong, Danny Enepekides, Kevin Higgins

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineHead and neckSurgeryAblationFree flap reconstructionCost analysisAblative caseRadiation therapy
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether simultaneous (ablation and reconstruction overlaps by two teams) head and neck reconstruction is cost effective compared to sequentially (ablation followed by reconstruction) performed surgery. STUDY DESIGN: Case-controlled study. SETTING: Tertiary care hospital. SUBJECTS: Oncology patients undergoing free flap reconstruction of the head and neck. METHODS: A match paired comparison study was performed with a retrospective chart review examining the total time of surgery for sequential and simultaneous surgery. Nine patients were selected for both the sequential and simultaneous groups. Sequential head and neck reconstruction patients were pair matched with patients who had undergone similar oncologic ablative or reconstructive procedures performed in a simultaneous fashion. A detailed cost analysis using the microcosting method was then undertaken looking at the direct costs of the surgeons, anesthesiologist, operating room, and nursing. RESULTS: On average, simultaneous surgery required 3 hours 15 minutes less operating time, leading to a cost savings of approximately $1200/case when compared to sequential surgery. This represents approximately a 15% reduction in the cost of the entire operation. CONCLUSION: Simultaneous head and neck reconstruction is more cost effective when compared to sequential 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.099
GPT teacher head0.285
Teacher spread0.186 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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