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Record W4401009665 · doi:10.1016/j.xjon.2024.07.015

Replacing post–chest tube removal chest radiographs with clinical assessment in adult thoracic surgery patients: A single-center prospective study

2024· article· en· W4401009665 on OpenAlexaff
Andreea Matei, Awrad Nasralla, Najib Safieddine, Sayf Gazala, Carmine Simone, Negar Ahmadi

Bibliographic record

VenueJTCVS Open · 2024
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChest tubeMedicineCardiothoracic surgerySingle CenterProspective cohort studyTube (container)SurgeryCenter (category theory)RadiologyNuclear medicinePneumothoraxMaterials science

Abstract

fetched live from OpenAlex

Objective: The necessity and utility of chest radiographs in the absence of clinical symptoms have been questioned after chest tube removal. This study aimed to evaluate the impact of replacing routine chest radiographs after chest tube removal with clinical observation on outcomes in patients undergoing elective thoracic surgery. Methods: This was a single-center prospective study of adult patients undergoing elective lung resection. Standard chest radiographs after chest tube removal were replaced with a clinical observation protocol for 2 hours after removal. Chest radiographs after chest tube removal were meant to be obtained only for symptomatic patients. The primary outcome was the incidence of adverse events related to this change. Secondary outcomes included changes in clinical management, length of stay, and postoperative complications. Results: < .05). Conclusions: Clinical observation can safely replace routine chest radiographs after chest tube removal in asymptomatic patients undergoing elective thoracic surgery. This approach may lead to shorter hospital stays and reduced healthcare costs without compromising patient safety. The findings support a clinically driven use of postoperative imaging in this patient population, highlighting the importance of individualized patient care.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.370
Teacher spread0.321 · 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
Published2024
Admission routes1
Has abstractyes

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