MétaCan
Menu
Back to cohort
Record W4205500468 · doi:10.1101/2022.01.03.22268707

Locations of burr holes are associated with recurrence after single burr hole drainage surgery for chronic subdural hematoma

2022· preprint· en· W4205500468 on OpenAlexaboutno aff
Hiroaki Hashimoto, Tomoyuki Maruo, Yuki Kimoto, Masami Nakamura, Takahiro Fujinaga, Yukitaka Ushio

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsChronic subdural hematomaMedicineSurgeryMidline shiftHematoma

Abstract

fetched live from OpenAlex

Abstract Objective This study aimed to reveal the relation between chronic subdural hematomas (CSDH) recurrence and locations of CSDH and burr holes. Methods Initial single burr hole surgeries for CSDH with a drainage tube between April 2005 and October 2021 at Otemae Hospital were enrolled. Patients’ medical records, CSDH volume, and CSDH computed tomography values (CTV) were evaluated. The locations of CSDH and burr holes were assessed using Montreal Neurological Institute coordinates. Results We enrolled 223 patients (bilateral CSDH in 34 patients), and 257 surgeries were investigated. Rate of CSDH recurrence requiring reoperation (RrR) was 13.5%. RrR rate was significantly higher in patients aged ≥76 years, bilateral CSDH, and postoperative hemiplegia. In RrR, preoperative CSDH volume was significantly larger, and CTV was significantly smaller. Locations of CSDH had no influence on recurrence. However, in RrR, locations of burr holes were more lateral and more ventral. Multivariate Cox proportional hazards regression analysis showed that bilateral CSDH, more ventral burr hole positions, and postoperative hemiplegia were risk factors for recurrence. Conclusions Locations of burr holes related to recurrence. In RrR, CSDH profiles had larger volume and less CTV. Hemiplegia after burr hole surgery is a warning sign for RrR.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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.

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuemedRxivSame topicNeurosurgical Procedures and ComplicationsFrench-language works237,207