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Record W2564781604

The Impact of Ontario's Bariatric Network on Health Services Utilization following Bariatric Surgery

2015· dissertation· en· W2564781604 on OpenAlexaboutno aff
Ahmad Elnahas

Bibliographic record

VenueTSpace (University of Toronto) · 2015
Typedissertation
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryRetrospective cohort studyObesityGeneral surgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

In 2009, the Ontario Bariatric Network (OBN) was created to address the increasing demand for out-of-country bariatric services. This study evaluated the impact of the OBN on postoperative hospital services use among Ontario residents who received bariatric surgery. A retrospective, before-and-after study was conducted. All Ontario residents who underwent funded first-time bariatric surgery were included. Hospital services use within one year following surgery was compared between the 3-year periods before (2007-2009) and after (2010-2012) the OBN. A total of 5,617 and 6,896 patients received bariatric surgery before and after the OBN, respectively. After adjustment, the OBN was associated with fewer postoperative hospital services (RR 0.83, 95%CI 0.78-0.89, P%%%%M.Sc.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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