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EVALUATION OF MEDLARS
Derhashar Swargiary
M.L.I.S. 3rd year
Pondicherry University
INTRODUCTION
F. W. Lancaster professor emeritus at the
University of Illinois Graduate School of Library
and Information Science and former NLM
employee is the Pioneer in Evaluation of Early
MEDLARS Systems.
 The evaluation of the MEDLARS Demand Search
Service in 1966 and 1967 was one of the earliest
evaluations of a computer-based retrieval system
and the first application of recall and precision
measures in a large, operational database
setting.

EVALUATION OF OPERATING EFFICIENCY OF
MEDLARS:


NLM conducted a wide-ranging program to
evaluate the performance of MEDLARS. It was
conducted in 1966-67.
PURPOSE
To study the demand search requirements of
MEDLARS users.
 To determine how effectively and efficiently the
present MEDLARS services are meeting its
requirements.
 To determine factors adversely effecting the
performance.
 To discover ways in which more effectively or
more economically user requirements can be
satisfied

THE PRIME REQUIREMENTS OF DEMAND SEARCH USERS
WERE PRESUMED TO RELATE THE FOLLOWING FACTORS

:

The coverage of MEDLARS(i.e. the proportion of
useful literature on a particular topic, within the
time limit imposed, i.e. indexed into the system)
 Its recall power(i.e. its ability to retrieve relevant
documents)
 The precision power(i.e. the ability to hold back
the non relevant items
 The response time of the system
 The format in which the search results are
presented
 The amount of effort the user must personally
expend in order to achieve a satisfactory response
from the system.

THE TWO MOST CRITICAL PROBLEMS FACED
IN THE EVALUATION OF MEDLARS WERE:
Ensuring that the body of test request was as far
as possible representative of the complete
spectrum of kinds of requests processed.
 Establishing methods for determining recall and
precision performance.

METHODOLOGY
target of 300 evaluated queries were needed to
provide an adequate test
 stratified sampling of the medical institutions
from which demands had come during 1965, and
processing queries received from the sample
institutions over a 12-month period.
 The twenty-one user groups were so selected.
 Some 410 queries were received from the user
group and processed, and finally 302 of these
were fully evaluated and used in the MEDLARS
test.





Queries were submitted to the MEDLARS and on
receipt of the queries; MEDLARS staff prepared a
search formulation (i.e. query designation) for each
query using an appropriate combination of MeSH
terms. A computer search was then carried out in the
normal way. At this stage each user was also asked to
submit a list of recent articles that he judged to be
relevant to his query. The
result of a search was a computer printout of
references. Since the total items retrieved might be
high (some searches retrieved more than 500
references), 25 to 30 items were selected randomly
from the list and photocopies of these were provided
to the searcher fro relevance assessment.
Each searcher was asked to go through the full
text of the articles and then to report about each
article on the following scale of relevance:
 H1 – of major value (relevance)
 H2 – of minor value
 W1 – of no value
 W2 – of value not assessable (for example, in a
foreign language).

EACH SEARCHER WAS ASKED TO GO THROUGH THE FULL TEXT OF THE
ARTICLES AND THEN TO REPORT ABOUT EACH ARTICLE ON THE FOLLOWING
SCALE OF RELEVANCE:



If L items were in the sample, the overall precision
ratio was 100(H1 + H2)/L and the ‘major value’
precision ratio were 100H1/L. It was obviously not
feasible to examine the whole MEDLARS database in
relation to each search in order to establish recall
ratio. Therefore, an indirect method was adopted for
calculation of recall ratio: Each user was asked to
identify relevant items for his query before receiving
the search output and then search was carried out to
find out whether those items were indexed in the
database and retrieved along with other items that
are both relevant and irrelevant. If t such relevant
items were identified by the user and available on the
database for a given query, and H were retrieved in
the search, the overall recall ratio and ‘major value’
recall ratio was estimated as 100H/t and 100H1/t1
respectively.
The next stage of the evaluation was an elaborate
analysis of retrieval failures, i.e., examining, for
each search, collected data concerning failures
include:
 a) Query statement;
 b) Search formulation;
 c) Index entries for a sample of ‘missed’ items (i.e.
relevant items that are not retrieved) and ‘waste’
items (i.e. noise—retrieval of irrelevant items);
and
 d) Full text (c).

RESULTS:


The average number of references retrieved for each search was 175, with an
average or overall precision ratio of 50.4%; that is, of the average 175 references
retrieved, about 87 were found to be not relevant. The overall recall ratio was
57.7% as calculated by an indirect method. Taking the average search, and
assuming that about 88 of the references found were relevant, with an overall
recall ratio of 57.7% implies that about 150 references should have been found,
but 62 were missed. However, the recall and precision ratios for each of the 302
searches were analyzed and individual ratios were then averaged in the
MEDLARS test. The results were:
Overall

Recall ratio

Major value

57.7%

65.2%

25.7%
Precision ratio

50.4%
CONCLUSIONS



The results of the MEDLARS test led to a series
of recommendations for the improvement of the
MEDLARS performance. Some notable changes
made to the MEDLARS as a result of this test
include design of a new search request form
(intended to ensure that a request statement is a
good reflection of the information need behind it);
and expansion of the entry vocabulary and
improvement of its accessibility, and the adoption
of an increased level of integration among
personnel involved in indexing, searching and
vocabulary control devices.
REFERENCES:
Jones, Karen Spark and Willet, Peter (1997)
Readings in Information Retrieval. USA :
Elsevier science.
 Retrieved from http://www.egyankosh.com on
October 13th, 2013.
 Lal, C. and Kumar, K. (2005).Understanding
basics of Library and information Science. New
Delhi : Ess Ess Publication.


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Evaluation of medlars

  • 1. EVALUATION OF MEDLARS Derhashar Swargiary M.L.I.S. 3rd year Pondicherry University
  • 2. INTRODUCTION F. W. Lancaster professor emeritus at the University of Illinois Graduate School of Library and Information Science and former NLM employee is the Pioneer in Evaluation of Early MEDLARS Systems.  The evaluation of the MEDLARS Demand Search Service in 1966 and 1967 was one of the earliest evaluations of a computer-based retrieval system and the first application of recall and precision measures in a large, operational database setting. 
  • 3. EVALUATION OF OPERATING EFFICIENCY OF MEDLARS:  NLM conducted a wide-ranging program to evaluate the performance of MEDLARS. It was conducted in 1966-67.
  • 4. PURPOSE To study the demand search requirements of MEDLARS users.  To determine how effectively and efficiently the present MEDLARS services are meeting its requirements.  To determine factors adversely effecting the performance.  To discover ways in which more effectively or more economically user requirements can be satisfied 
  • 5. THE PRIME REQUIREMENTS OF DEMAND SEARCH USERS WERE PRESUMED TO RELATE THE FOLLOWING FACTORS : The coverage of MEDLARS(i.e. the proportion of useful literature on a particular topic, within the time limit imposed, i.e. indexed into the system)  Its recall power(i.e. its ability to retrieve relevant documents)  The precision power(i.e. the ability to hold back the non relevant items  The response time of the system  The format in which the search results are presented  The amount of effort the user must personally expend in order to achieve a satisfactory response from the system. 
  • 6. THE TWO MOST CRITICAL PROBLEMS FACED IN THE EVALUATION OF MEDLARS WERE: Ensuring that the body of test request was as far as possible representative of the complete spectrum of kinds of requests processed.  Establishing methods for determining recall and precision performance. 
  • 7. METHODOLOGY target of 300 evaluated queries were needed to provide an adequate test  stratified sampling of the medical institutions from which demands had come during 1965, and processing queries received from the sample institutions over a 12-month period.  The twenty-one user groups were so selected.  Some 410 queries were received from the user group and processed, and finally 302 of these were fully evaluated and used in the MEDLARS test. 
  • 8.   Queries were submitted to the MEDLARS and on receipt of the queries; MEDLARS staff prepared a search formulation (i.e. query designation) for each query using an appropriate combination of MeSH terms. A computer search was then carried out in the normal way. At this stage each user was also asked to submit a list of recent articles that he judged to be relevant to his query. The result of a search was a computer printout of references. Since the total items retrieved might be high (some searches retrieved more than 500 references), 25 to 30 items were selected randomly from the list and photocopies of these were provided to the searcher fro relevance assessment.
  • 9. Each searcher was asked to go through the full text of the articles and then to report about each article on the following scale of relevance:  H1 – of major value (relevance)  H2 – of minor value  W1 – of no value  W2 – of value not assessable (for example, in a foreign language). 
  • 10. EACH SEARCHER WAS ASKED TO GO THROUGH THE FULL TEXT OF THE ARTICLES AND THEN TO REPORT ABOUT EACH ARTICLE ON THE FOLLOWING SCALE OF RELEVANCE:  If L items were in the sample, the overall precision ratio was 100(H1 + H2)/L and the ‘major value’ precision ratio were 100H1/L. It was obviously not feasible to examine the whole MEDLARS database in relation to each search in order to establish recall ratio. Therefore, an indirect method was adopted for calculation of recall ratio: Each user was asked to identify relevant items for his query before receiving the search output and then search was carried out to find out whether those items were indexed in the database and retrieved along with other items that are both relevant and irrelevant. If t such relevant items were identified by the user and available on the database for a given query, and H were retrieved in the search, the overall recall ratio and ‘major value’ recall ratio was estimated as 100H/t and 100H1/t1 respectively.
  • 11. The next stage of the evaluation was an elaborate analysis of retrieval failures, i.e., examining, for each search, collected data concerning failures include:  a) Query statement;  b) Search formulation;  c) Index entries for a sample of ‘missed’ items (i.e. relevant items that are not retrieved) and ‘waste’ items (i.e. noise—retrieval of irrelevant items); and  d) Full text (c). 
  • 12. RESULTS:  The average number of references retrieved for each search was 175, with an average or overall precision ratio of 50.4%; that is, of the average 175 references retrieved, about 87 were found to be not relevant. The overall recall ratio was 57.7% as calculated by an indirect method. Taking the average search, and assuming that about 88 of the references found were relevant, with an overall recall ratio of 57.7% implies that about 150 references should have been found, but 62 were missed. However, the recall and precision ratios for each of the 302 searches were analyzed and individual ratios were then averaged in the MEDLARS test. The results were: Overall Recall ratio Major value 57.7% 65.2% 25.7% Precision ratio 50.4%
  • 13. CONCLUSIONS  The results of the MEDLARS test led to a series of recommendations for the improvement of the MEDLARS performance. Some notable changes made to the MEDLARS as a result of this test include design of a new search request form (intended to ensure that a request statement is a good reflection of the information need behind it); and expansion of the entry vocabulary and improvement of its accessibility, and the adoption of an increased level of integration among personnel involved in indexing, searching and vocabulary control devices.
  • 14. REFERENCES: Jones, Karen Spark and Willet, Peter (1997) Readings in Information Retrieval. USA : Elsevier science.  Retrieved from http://www.egyankosh.com on October 13th, 2013.  Lal, C. and Kumar, K. (2005).Understanding basics of Library and information Science. New Delhi : Ess Ess Publication. 