MétaCan
Menu
Back to cohort
Record W4393383844 · doi:10.3171/2024.2.focus2445

The military assignations of Thierry de Martel (1875–1940), French neurosurgery pioneer, during World War I

2024· article· en· W4393383844 on OpenAlexaff
Johan Pallud, Angela Rita Elia, Alexandre Roux, Marc Zanello

Bibliographic record

VenueNeurosurgical FOCUS · 2024
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsSte. Anne's Hospital
Fundersnot available
KeywordsInfantryBattleBattlefieldWorld War IISpanish Civil WarFirst world warHistoryMedicineHumanitiesArtAncient historyArchaeology

Abstract

fetched live from OpenAlex

hierry de Martel (1875de Martel ( -1940) ) owes his international fame to the developments he made in neurosurgery and his acknowledged role as a neurosurgery pioneer in France.1,2 During World War I (WWI), he put his patriotic energy and his surgical skills into the service of French soldiers.1 Thierry de Martel was mobilized into the French military on August 1, 1914, at the beginning of the war.He was first incorporated into the 99th Infantry Regiment as a second-class medical assistant, but managed to be transferred to the 292nd Infantry Regiment to reach the battlefield.2,3 On October 3, 1914, during the First Battle of the Aisne, he was wounded in the thigh by shrapnel and repatriated to Paris.We lack information on the wound and convalescence of de Martel.We have recently discovered a letter from Gyp, his mother, who described additional details of the wound he received.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0180.004

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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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 designNot applicable
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

Explore more

Same venueNeurosurgical FOCUSSame topicHistory of Medical PracticeFrench-language works237,207