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Record W4220787085 · doi:10.1136/rapm-2022-103487

Catheter-guided multilevel epidural blood patches in an adolescent boy

2022· article· en· W4220787085 on OpenAlexafffund
Laurel Walfish, Sultan Alobaikan, Zubin Lahijanian, Christine Saint‐Martin, Maria delPilar Cortes Nino, Pablo Ingelmo

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurosurgical Procedures and Complications
Canadian institutionsMontreal Neurological Institute and HospitalMontreal Children's HospitalMcGill University Health Centre
FundersFondation de l'Hôpital de Montréal pour enfants
KeywordsMedicineEpidural blood patchCerebrospinal fluidCatheterAnesthesiaSurgeryCerebrospinal fluid leakPopulationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is increasing evidence for the use of multilevel epidural catheter-guided blood patches to treat spontaneous cerebrospinal fluid leaks in adults. Yet, there are scarce data for their use in children. Furthermore, higher level epidural blood patches are uncommon in both adult and pediatric populations. CASE PRESENTATION: An adolescent boy with multilevel cerebrospinal fluid leaks associated with status migrainosus failed conservative pain management treatment. As he remained severely symptomatic, epidural blood patches were required to mitigate his symptoms. Given his multilevel leaks, a catheter-guided blood patch approach was used to treat multiple cervicothoracic and thoracolumbar leaks. After three separate uneventful blood patch procedures, the patient was free of symptoms. Other than some rebound intracranial hypertension, the patient maintained full neurological capacity without further complications. Following the resolution of treatment, he has not sought help for pain since his last blood patch over a year ago. CONCLUSIONS: Multilevel catheter-guided blood patches have the potential to be administered to higher level spinal regions to the pediatric population suffering from multiple spontaneous cerebrospinal fluid leaks.

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 score0.168
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.072
GPT teacher head0.310
Teacher spread0.238 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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