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Record W2027855264 · doi:10.1097/ijg.0b013e31819c485b

Digital Ocular Massage for Hypertensive Phase After Ahmed Valve Surgery

2010· article· en· W2027855264 on OpenAlexaff
Michael Smith, Noa Geffen, Tariq Alasbali, Yvonne M. Buys, Graham E. Trope

Bibliographic record

VenueJournal of Glaucoma · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMassageMedicineIntraocular pressureGlaucomaOphthalmologySurgery

Abstract

fetched live from OpenAlex

PURPOSE: To examine the role of ocular massage during the hypertensive phase after Ahmed valve surgery METHODS: Nonrandomized prospective study. RESULTS: Eighteen patients with intraocular pressure (IOP) above target 1 to 8 weeks after Ahmed glaucoma drainage device surgery underwent digital ocular massage. The mean IOP 1 hour after massage was 4.3 mm Hg lower than before massage (18.8%, P=0.0008). We used a 20% reduction in IOP at 1-hour postmassage to differentiate responders from nonresponders and by this definition 50% responded to ocular massage. One patient (5.6%) responded well but was unable to perform massage at home. The remaining 8 patients (44.4%) performed regular digital massage and the 20% drop in IOP was maintained at the 2-week, 6-week, and 6-month review, although by 6 months 50% required glaucoma drops to achieve target IOP. There were no massage-associated complications in this series. CONCLUSIONS: Digital ocular massage has a useful role to play in the management of the hypertensive phase after Ahmed glaucoma drainage device surgery. In this series 50% of patients achieved a 20% drop in IOP with massage.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.021
GPT teacher head0.241
Teacher spread0.219 · 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 designCase report
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

Citations22
Published2010
Admission routes1
Has abstractyes

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