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Record W2908166195 · doi:10.1017/s026646231800288x

PP164 Identifying Complications Of Partial Nephrectomy Using Physician Claims

2018· article· en· W2908166195 on OpenAlexaboutno aff
Jian Sun, Tania Stafinski, Fernanda Inagaki Nagase, Devidas Menon

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNephrectomyAmbulatoryComplicationCohortSurgeryPopulationKidney cancerIncidence (geometry)Laparoscopic surgeryGeneral surgeryCancerLaparoscopyInternal medicineKidney

Abstract

fetched live from OpenAlex

Introduction: Many population-based studies identify surgical complications using hospital discharge abstract databases (DAD). With DAD, however, complications occurring after the discharge date cannot be followed up. This study used physician claims data to identify the complications of partial nephrectomy, and to compare the rates of complications of open, laparoscopic, and robot-assisted nephrectomies. Methods: Physician claims, DAD, and ambulatory care data from April 2003 to March 2016 were provided by Alberta Health. DAD and ambulatory care data were used to extract information on patients with kidney cancer who underwent partial nephrectomy. All physician claims within 30 days before and after surgery for the cohort were extracted. The numbers of the same International Classification of Diseases, Ninth Revision (ICD-9), codes before and after surgery were compared. If a number increased after surgery, this diagnosis was initially identified as a complication. All diagnoses with neoplasms were excluded. The incidence rates of complications for the three surgery groups were calculated. Chi-squared tests were conducted for the following nephrectomy comparisons: laparoscopic versus open; robot-assisted versus open; and robot-assisted versus laparoscopic. Results: A total of 1,890 kidney cancer patients had partial nephrectomies. Among them, 1,080, 411, and 399 had open, laparoscopic, and robot-assisted nephrectomies, respectively. One patient who had two different nephrectomies on the same day was excluded from analysis. The robot-assisted group had lower rates of digestive complications (ICD-9: 537–578, 787, 789, 998.6) and infections (ICD-9: 004–041, 998.5) than the open group, and higher rates of genitourinary complications (ICD-9: 584–599, 788, 997.5) than the laparoscopy group. The robot-assisted group had lower rates than the open group for most of the complication categories, but the differences were not statistically significant. Conclusions: Robot-assisted surgery appears to be superior to open surgery, but no better than laparoscopic surgery, in terms of minimizing the risk of complications following partial nephrectomy.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.056
GPT teacher head0.454
Teacher spread0.398 · 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
Published2018
Admission routes1
Has abstractyes

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