MétaCan
Menu
Back to cohort
Record W2136495559 · doi:10.12927/hcq.2013.21131

Variability in the Surgical Management of Carpal Tunnel Syndrome: Implications for the Effective Use of Healthcare Resources

2009· article· en· W2136495559 on OpenAlexaff
Amr Elmaraghy, Moira Devereaux

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsSt Joseph's Health Centre
Fundersnot available
KeywordsCarpal tunnel releaseDiscretionMedicineHealth careCarpal tunnel syndromeBest practiceControl (management)Medical emergencyOperations managementSurgeryManagement

Abstract

fetched live from OpenAlex

Medicine has been said to be as much art as science, where physicians invoke their individual skills and judgment to address the unique aspects of each presenting patient. Yet to what extent should physicians exercise their own discretion in determining the use rates of hospital resources? This article examines the results of a study on surgeon use of surgical setting and anesthetic technique for carpal tunnel release (CTR) surgery - a simple, low-risk surgical procedure that can be performed in either a formal operating room or a minor surgical setting, using local, regional or general anesthetic. The selected combination of surgical setting and anesthetic technique employed by a surgeon has not been standardized and can significantly impact both patient outcomes and administrative healthcare costs for hospital resources, equipment and pharmaceuticals. While a certain amount of variability in surgical management is necessary to allow clinicians to practise their "art," policy makers have an opportunity to standardize some surgeon practices to control costs, particularly when those practices are found to be as strongly influenced by the subjective attitudes of individual surgeons as by evidence-based science and economics.

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 score0.775
Threshold uncertainty score0.464

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 head0.031
GPT teacher head0.342
Teacher spread0.311 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueHealthcare QuarterlySame topicPeripheral Nerve DisordersFrench-language works237,207