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Record W1581217844 · doi:10.1503/cjs.017412

Quality of inguinal hernia operative reports: room for improvement

2013· article· en· W1581217844 on OpenAlexaffvenue
William J. Grace, Amandeep Pooni, Shawn Forbes, Cagla Eskicioglu, Emily Pearsall, Fred Brenneman, Robin S. McLeod

Bibliographic record

VenueCanadian Journal of Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsSunnybrook Health Science CentreMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineChecklistAuditInguinal herniaHerniaGeneral surgerySurgeryAntibiotic prophylaxisAntibiotics

Abstract

fetched live from OpenAlex

BACKGROUND: Operative reports (ORs) serve as the official documentation of surgical procedures. They are essential for optimal patient care, physician accountability and billing, and direction for clinical research and auditing. Nonstandardized narrative reports are often of poor quality and lacking in detail. We sought to audit the completeness of narrative inguinal hernia ORs. METHODS: A standardized checklist for inguinal hernia repair (IHR) comprising 33 variables was developed by consensus of 4 surgeons. Five high-volume IHR surgeons categorized items as essential, preferable or nonessential. We audited ORs for open IHR at 6 academic hospitals. RESULTS: We audited 213 ORs, and we excluded 7 femoral hernia ORs. Tension-free repairs were the most common (82.5%), and the plug-and-patch technique was the most frequent (52.9%). Residents dictated 59% of ORs. Of 33 variables, 15 were considered essential and, on average, 10.8 ± 1.3 were included. Poorly reported elements included first occurrence versus recurrent repair (8.3%), small bowel viability in incarcerated hernias (10.7%) and occurrence of intraoperative complications (32.5%). Of 18 nonessential elements, deep vein thrombosis prophylaxis, preoperative antibiotics and urgency were reported in 1.9%, 11.7% and 24.3% of ORs, respectively. Repair-specific details were reported in 0 to 97.1% of ORs, including patch sutured to tubercle (55.1%) and location of plug (67.0%). CONCLUSION: Completeness of IHR ORs varied with regards to essential and nonessential items but were generally incomplete, suggesting there is opportunity for improvement, including implementation of a standardized synoptic OR.

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)0.142
metaresearch head score (Gemma)0.352
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.752

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1420.352
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.013
Science and technology studies0.0020.004
Scholarly communication0.0110.015
Open science0.0050.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.060
GPT teacher head0.329
Teacher spread0.269 · 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.

Study designObservational
DomainReporting
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

Citations15
Published2013
Admission routes2
Has abstractyes

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