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Record W2152004988

Tumores intracraneanos del niño

2006· article· es· W2152004988 on OpenAlexaboutno aff
Fernando Chico-Ponce de León, Eduardo Castro-Sierra, Mario Pérezpeña-Díazconti, Luis Felipe Gordillo-Domínguez, Blanca Lilia Santana-Montero, Luis E Rocha-Rivero, Miguel Angel Vaca-Ruiz, Marcos Ríos-Alanís, Federico Sánchez-Herrera, Ricardo Valdés-Orduño

Bibliographic record

VenueBoletín Médico del Hospital Infantil de México · 2006
Typearticle
Languagees
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

Introduccion. Los tumores intracraneanos (TIC) pediatricos son las neoplasias solidas mas frecuentes en ninos. Se presenta la experiencia del Hospital Infantil de Mexico Federico Gomez (HIMFG) de los ultimos 36 anos. Material y metodos. Se utilizaron los siguientes archivos del HIMFG: Clinico, de los Departamentos de Neurocirugia y de Patologia. Los resultados se compararon con los datos del Hospital for Sick Children de Toronto y del Instituto Nacional de Pediatria de Mexico, D. F. Resultados. En el HIMFG, 55% de los pacientes eran del sexo masculino. Predominaron desde lactantes mayores hasta escolares, con mas de 50%. Los tumores fueron: 397 supratentoriales y 413 infratentoriales. Los mas frecuentes fueron: astrocitomas (32%), meduloblastomas (19%), craneofaringiomas (11%) y ependimomas (10%); en el quinto lugar quedaron los germinomas (4%). Los gliomas mixtos, los meningiomas, los tumores neuroectodermicos primitivos y los ependimoblastomas representaron de 1 a 3%. Conclusiones. En el HIMFG, los 4 tipos mas frecuentes de tumor fueron: astrocitomas, meduloblastomas, craneofaringiomas y ependimomas. El trabajo de campo del HIMFG ha tenido un desarrollo exponencial desde la mitad de los anos setenta. Actualmente, el volumen de pacientes manejado por el HIMFG es semejante a, o rebasa discretamente, al de otras instituciones.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

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