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Record W30840056 · doi:10.1520/jfs14868j

The State of Electroconvulsive Therapy in Texas. Part 2: Contact with Physicians, Hospitals, Medical Liability Insurance Companies, and Manufacturers of Stimulus Generating Equipment

2000· article· en· W30840056 on OpenAlexaboutno aff
Valentina Scarano, AR Felthous, Terry Early

Bibliographic record

VenueJournal of Forensic Sciences · 2000
Typearticle
Languageen
FieldMedicine
TopicElectroconvulsive Therapy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsElectroconvulsive therapyLiabilityLiability insuranceState governmentMedicineMedical emergencyQuarter (Canadian coin)Stimulus (psychology)BusinessFamily medicinePsychiatryPsychologyFinanceLawLocal governmentPolitical science

Abstract

fetched live from OpenAlex

Since mid-1993, all ECT treatments performed in the state of Texas (except for United States government hospitals) must be reported every quarter to the Texas Department of Mental Health and Mental Retardation (TXMHMR) on a data collection form provided by the Department. Part 1 of this paper reviewed that data. This paper reviews the responses to questionnaires and contacts made with physicians, hospitals, medical liability insurance companies, and manufacturers of stimulus generating devices regarding their experience with ECT in Texas. Questionnaires were sent to physicians and hospitals that had not performed ECT during the final two quarters of the review period. Medical liability insurance companies and the manufacturers of the stimulus generating equipment used in ECT were contacted regarding their experience with liability claims. The results indicate that medical liability in regards to the performance of ECT is extremely low. Physicians and hospitals that stopped performing ECT did so for reasons other than medical liability.

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)0.001
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: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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

Citations2
Published2000
Admission routes1
Has abstractyes

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