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Record W229668847 · doi:10.1177/229255030601400306

Replantation Surgery in Quebec: The Bottlenecks to Rapid Care

2006· article· en· W229668847 on OpenAlexaffvenueabout
Daniel E. Borsuk, Teanoosh Zadeh, Chen Lee, Karl Moore, Grace Hwei Ching Tan

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsBell (Canada)McGill University
Fundersnot available
KeywordsReplantationMedicineAmputationSurgeryHand surgeryGeneral surgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Time delays resulting in prolonged ischemia have a significant impact on the successful reattachment of amputated body parts. No studies have addressed the issues surrounding delays from the time of the accident to the start of replantation surgery. The present paper identifies the bottlenecks that prolong the time before patients are able to gain access to a replant team. METHODS: A total of 50 patients underwent microsurgical replantation, because of traumatic amputation, at a university-based hospital from 1996 to 2003. The charts were analyzed to ascertain individual time intervals from the onset of injury until the beginning of replant surgery. RESULTS: The average length of time for patients who came directly to the replant centre was 3 h 40 min before surgery began. In contrast, for those referred from outlying hospitals, the elapsed time was 6 h 21 min. CONCLUSIONS: Two major bottlenecks were found. First, for patients who were referred from other health centres, delays were due to a lack of information as to where patients could receive appropriate replant surgery. Second, delays at the replant centre were primarily due to insufficient physical and human resources in the operating room.

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 score0.101
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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

Citations1
Published2006
Admission routes3
Has abstractyes

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