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Record W2082274370 · doi:10.1136/bmj.331.7528.1338-b

Challenges of private provision in the NHS

2005· letter· en· W2082274370 on OpenAlexaboutno aff
Catherine Guly, Richard Sidebottom, K N Hakin, Keith Bates

Bibliographic record

VenueBMJ · 2005
Typeletter
Languageen
FieldHealth Professions
TopicHealth Services Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCataract surgeryOptometryMedical emergencySurgery

Abstract

fetched live from OpenAlex

Editor—Timmins questions whether the benefits of using independent providers for health care outweighs the risks.1 He notes the tendency for treatment centres to take on simpler cases, leaving the NHS to deal with complex surgery, but he brushes over the devastating effect that this is having on surgical training.1 Cataract surgery is the most common operation performed by treatment centres. It takes intensive training to become a good cataract surgeon. It is usually possible to predict which cataract operations are going to be difficult or high risk when the patient is seen before the procedure.2 In our department, these complex cases are listed as “consultant to do.” The remainder are listed as “any surgeon to do,” and it is these patients who may be suitable for training.​training. Figure 1 Credit: PASCAL GOETGHELUCK/SPL Since Netcare, a mobile treatment unit, and the Shepton Mallet treatment centre started operating in Somerset, we have noticed a dramatic reduction in training opportunities for cataract surgery. The number of “any surgeon to do” patients on each consultant list has halved from three patients per operating list in 2003 to 1.5 patients per list in 2005. Trainees are often unable to operate because of a lack of suitable cases. This will affect all ophthalmic training grades, but particularly senior house officers. Fielder and Watson, noting that Action on Cataracts had failed to consider surgical training, made some excellent suggestions about how training could be improved.3 Their ideas of high volume service and low volume training surgical lists, with blocks of intensive surgical training seem eminently sensible. The demand for surgery was apparently overestimated when planning treatment centres.1 Could the NHS now use this excess capacity in the form of low volume surgical training lists? It seems very “short sighted” that, although the number of cataract operations performed in the UK is increasing, the future of cataract surgery training is under threat.

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)0.018
metaresearch head score (Gemma)0.060
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.036
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0110.015
Scholarly communication0.0190.021
Open science0.0040.009
Research integrity0.0360.039
Insufficient payload (model declined to judge)0.0280.006

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 head0.182
GPT teacher head0.498
Teacher spread0.316 · 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
GenreCommentary

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

Citations7
Published2005
Admission routes1
Has abstractyes

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