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MINIMALLY INVASIVE PARATHYROIDECTOMY: AN AUDIT OF A CHANGE IN CLINICAL PRACTICE

2007· article· en· W2079981776 on OpenAlexaff
Ming Yew, Ivan J. Thompson

Bibliographic record

VenueANZ Journal of Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsMedicineParathyroidectomyPrimary hyperparathyroidismCure rateAuditSurgeryGeneral surgeryInvasive surgeryHyperparathyroidismParathyroid hormoneInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Minimally invasive parathyroidectomy (MIP) for primary hyperparathyroidism is gaining acceptance as a useful tool in the armamentarium of the endocrine surgeon. METHODS: We undertook an audit of 154 consecutive cases of parathyroidectomy carried out through bilateral neck exploration as well as a minimally invasive approach. RESULTS: Bilateral neck exploration had a 100% single operation cure rate. MIP had a 90% cure rate. Sestamibi localization had a positive predictive value of 99% for identifying an abnormal parathyroid gland. However, it performed poorly in the presence of multiglandular disease, resulting in these patients being at risk of having persistent hyperparathyroidism and therefore requiring a second operation. CONCLUSION: Our results with bilateral neck exploration are favourable compared with other large series. However, we have reported a 10% reoperation rate with MIP. Although not ideal, we are confident that, as a result of improvements based on this audit and with increasing experience, the cure rate will improve to reach international benchmarks. As such we feel that this strategy is a pragmatic way to offer MIP to patients in our region.

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)0.003
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 score0.020
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.175
GPT teacher head0.429
Teacher spread0.254 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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