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Record W2111172661 · doi:10.1017/s0317167100004510

Management of Chronic Subdural Hematoma: A National Survey and Literature Review

2005· article· en· W2111172661 on OpenAlexaffvenueabout
Aleksa Cenic, Mohit Bhandari, Kesava Reddy

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2005
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineCraniotomyChronic subdural hematomaNeurosurgeryHematomaSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To survey neurosurgical practices in the treatment of chronic and subacute subdural hematoma in the Canadian adult population. METHODS: We developed and administered a questionnaire to Canadian Neurosurgeons with questions relating to the management of chronic and subacute subdural hematoma. Our sampling frame included all neurosurgery members of the Canadian Neurosurgical Society. RESULTS: Of 158 questionnaires, 120 were returned (response rate = 76%). The respondents were neurosurgeons with primarily adult clinical practices (108/120). Surgeons preferred one and two burr-hole craniostomy to craniotomy or twist-drill craniostomy as the procedure of choice for initial treatment of subdural hematoma (35.5% vs 49.5% vs 4.7% vs 9.3%, respectively). Craniotomy and two burr-holes were preferred for recurrent subdural hematomas (43.3% and 35.1%, respectively). Surgeons preferred irrigation of the subdural cavity (79.6%), use of a subdural drain (80.6%), and no use of anti-convulsants or corticosteroids (82.1% and 86.6%, respectively). We identified a lack of consensus with keeping patients supine following surgery and post-operative antibiotic use. CONCLUSION: Our survey has identified variations in practice patterns among Canadian Neurosurgeons with respect to treatment of subacute or chronic subdural hematoma (SDH). Our findings support the need for further prospective studies and clinical trials to resolve areas of discrepancies in clinical management and hence, standardize treatment regimens.

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)0.002
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: none
Teacher disagreement score0.720
Threshold uncertainty score0.563

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.017
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.040
GPT teacher head0.310
Teacher spread0.270 · 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

Citations172
Published2005
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicNeurosurgical Procedures and ComplicationsFrench-language works237,207